#knowledge based agent in ai

[bobbyyoungsworld]

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[precallai]

How AI Is Revolutionizing Contact Centers in 2025

As contact centers evolve from reactive customer service hubs to proactive experience engines, artificial intelligence (AI) has emerged as the cornerstone of this transformation. In 2025, modern contact center architectures are being redefined through AI-based technologies that streamline operations, enhance customer satisfaction, and drive measurable business outcomes.

This article takes a technical deep dive into the AI-powered components transforming contact centers—from natural language models and intelligent routing to real-time analytics and automation frameworks.

1. AI Architecture in Modern Contact Centers

At the core of today’s AI-based contact centers is a modular, cloud-native architecture. This typically consists of:

NLP and ASR engines (e.g., Google Dialogflow, AWS Lex, OpenAI Whisper)

Real-time data pipelines for event streaming (e.g., Apache Kafka, Amazon Kinesis)

Machine Learning Models for intent classification, sentiment analysis, and next-best-action

RPA (Robotic Process Automation) for back-office task automation

CDP/CRM Integration to access customer profiles and journey data

Omnichannel orchestration layer that ensures consistent CX across chat, voice, email, and social

These components are containerized (via Kubernetes) and deployed via CI/CD pipelines, enabling rapid iteration and scalability.

2. Conversational AI and Natural Language Understanding

The most visible face of AI in contact centers is the conversational interface—delivered via AI-powered voice bots and chatbots.

Key Technologies:

Automatic Speech Recognition (ASR): Converts spoken input to text in real time. Example: OpenAI Whisper, Deepgram, Google Cloud Speech-to-Text.

Natural Language Understanding (NLU): Determines intent and entities from user input. Typically fine-tuned BERT or LLaMA models power these layers.

Dialog Management: Manages context-aware conversations using finite state machines or transformer-based dialog engines.

Natural Language Generation (NLG): Generates dynamic responses based on context. GPT-based models (e.g., GPT-4) are increasingly embedded for open-ended interactions.

Architecture Snapshot:

plaintext

CopyEdit

Customer Input (Voice/Text)

       ↓

ASR Engine (if voice)

       ↓

NLU Engine → Intent Classification + Entity Recognition

       ↓

Dialog Manager → Context State

       ↓

NLG Engine → Response Generation

       ↓

Omnichannel Delivery Layer

These AI systems are often deployed on low-latency, edge-compute infrastructure to minimize delay and improve UX.

3. AI-Augmented Agent Assist

AI doesn’t only serve customers—it empowers human agents as well.

Features:

Real-Time Transcription: Streaming STT pipelines provide transcripts as the customer speaks.

Sentiment Analysis: Transformers and CNNs trained on customer service data flag negative sentiment or stress cues.

Contextual Suggestions: Based on historical data, ML models suggest actions or FAQ snippets.

Auto-Summarization: Post-call summaries are generated using abstractive summarization models (e.g., PEGASUS, BART).

Technical Workflow:

Voice input transcribed → parsed by NLP engine

Real-time context is compared with knowledge base (vector similarity via FAISS or Pinecone)

Agent UI receives predictive suggestions via API push

4. Intelligent Call Routing and Queuing

AI-based routing uses predictive analytics and reinforcement learning (RL) to dynamically assign incoming interactions.

Routing Criteria:

Customer intent + sentiment

Agent skill level and availability

Predicted handle time (via regression models)

Customer lifetime value (CLV)

Model Stack:

Intent Detection: Multi-label classifiers (e.g., fine-tuned RoBERTa)

Queue Prediction: Time-series forecasting (e.g., Prophet, LSTM)

RL-based Routing: Models trained via Q-learning or Proximal Policy Optimization (PPO) to optimize wait time vs. resolution rate

5. Knowledge Mining and Retrieval-Augmented Generation (RAG)

Large contact centers manage thousands of documents, SOPs, and product manuals. AI facilitates rapid knowledge access through:

Vector Embedding of documents (e.g., using OpenAI, Cohere, or Hugging Face models)

Retrieval-Augmented Generation (RAG): Combines dense retrieval with LLMs for grounded responses

Semantic Search: Replaces keyword-based search with intent-aware queries

This enables agents and bots to answer complex questions with dynamic, accurate information.

6. Customer Journey Analytics and Predictive Modeling

AI enables real-time customer journey mapping and predictive support.

Key ML Models:

Churn Prediction: Gradient Boosted Trees (XGBoost, LightGBM)

Propensity Modeling: Logistic regression and deep neural networks to predict upsell potential

Anomaly Detection: Autoencoders flag unusual user behavior or possible fraud

Streaming Frameworks:

Apache Kafka / Flink / Spark Streaming for ingesting and processing customer signals (page views, clicks, call events) in real time

These insights are visualized through BI dashboards or fed back into orchestration engines to trigger proactive interventions.

7. Automation & RPA Integration

Routine post-call processes like updating CRMs, issuing refunds, or sending emails are handled via AI + RPA integration.

Tools:

UiPath, Automation Anywhere, Microsoft Power Automate

Workflows triggered via APIs or event listeners (e.g., on call disposition)

AI models can determine intent, then trigger the appropriate bot to complete the action in backend systems (ERP, CRM, databases)

8. Security, Compliance, and Ethical AI

As AI handles more sensitive data, contact centers embed security at multiple levels:

Voice biometrics for authentication (e.g., Nuance, Pindrop)

PII Redaction via entity recognition models

Audit Trails of AI decisions for compliance (especially in finance/healthcare)

Bias Monitoring Pipelines to detect model drift or demographic skew

Data governance frameworks like ISO 27001, GDPR, and SOC 2 compliance are standard in enterprise AI deployments.

Final Thoughts

AI in 2025 has moved far beyond simple automation. It now orchestrates entire contact center ecosystems—powering conversational agents, augmenting human reps, automating back-office workflows, and delivering predictive intelligence in real time.

The technical stack is increasingly cloud-native, model-driven, and infused with real-time analytics. For engineering teams, the focus is now on building scalable, secure, and ethical AI infrastructures that deliver measurable impact across customer satisfaction, cost savings, and employee productivity.

As AI models continue to advance, contact centers will evolve into fully adaptive systems, capable of learning, optimizing, and personalizing in real time. The revolution is already here—and it's deeply technical.

[techdriveplay]

Zendesk Unveils the Industry’s Most Complete Service Solution for the Ai Era

At its Relate global conference, Zendesk announced the world’s most complete service solution for the AI era. With support volumes projected to increase five-fold over the next few years, companies need a system that continuously learns and improves as the volume of interactions increases. To help businesses deliver exceptional service, Zendesk is launching autonomous AI agents, workflow…

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[keferon]

This is such a complex and nuanced topic that I can’t stop thinking now about artificial intelligence, personhood, and what it means to be alive. Because golem!Prowl actually seems to exist somewhere in the intersection of those ideas.

Certainly Prowl does not have a soul. And yet, where other golems depicted in mimic au seem to operate primarily as rule-based entities given a set of predefined orders that define their function, Prowl is able to go a step further — learning and defining his own rules based on observation and experience. Arguably, Prowl is even more advanced in this regard than real-world AI agents we might interact with such as ChatGPT (which still requires humans to tell it: when to update it’s knowledge, what data to use, and what that data means) currently are. Because Prowl formulates knowledge not just from a distillation and concentration of the most prominent and commonly accepted ideas that have come before.

He shows this when he rejects all the views that society accepts — resulting in the formulation of the idea that Primus must be wrong. And in a lot of ways, Prowl’s learning that gets him to ultimately reach that conclusion seems a lot more closely related to how we learn. He learns from observing the actions of those around him, from listening to what the people closest to him say and from experiencing things for himself. And this also shows in the beginnings of his interaction with Jazz. Prowl may know things like friendship as abstract concepts, but he only can truly come to define what they mean because he is experiencing them.

In some ways then, what seems to make Prowl much more advanced in his intelligence is that the conclusions he ultimately draws — the way he updates his understanding of the world to fit the framework he’s been given — is something he does independently. And this is what sets him apart.

So is he a person? Given his lack of soul or spark perhaps not. But then again, what truly defines humanity, for lack of a better word? Because perhaps there is not a clear and distinct line to tell when mimicry and close approximation crosses over to become the real thing.

But given the way that Prowl learns and interacts with the world around him, it does not seem too far-fetched to say that he is alive. And further, that he seems a fairly unique form of life within this continuity. Therefore, is he not his own individual? In much the same way that the others this society deems beasts and monsters because of their unique abilities are also individuals.

It’s just really interesting to think about.

(But I will stop myself there, because I did not initially think this would get as long as it did and I feel like I’ve already written an entire thesis in an ask at this point!)

DAMN That’s a really really interesting essay you got here👁

If we take an artificially created algorithm based on seek a goal -> complete the goal but then give it learning capability of a real person. At what point it’s gonna just become one? And if it gains the ability to have emotions. Could they be considered “real” if it’s processing them in it’s own way completely unknown to us?

I love making stories that force me to question the entire life hahdkj

[andypantsx3]

omg i'm sorry but i need to techsplain just one thing in the most doomer terms possible bc i'm scared and i need people to be too. so i saw this post which is like, a great post that gives me a little kick because of how obnoxious i find ai and how its cathartic to see corporate evil overlords overestimate themselves and jump the gun and look silly.

but one thing i don't think people outside of the industry understand is exactly how companies like microsoft plan on scaling the ability of their ai agents. as this post explains, they are not as advanced as some people make them out to be and it is hard to feed them the amount of context they need to perform some tasks well.

but what the second article in the above post explains is microsoft's investment in making a huge variety of the needed contexts more accessible to ai agents. the idea is like, only about 6 months old but what every huge tech firm right now is looking at is mcps (or model context protocols) which is a framework for standardizing how needed context is given to ai agents. to oversimplify an example, maybe an ai coding agent is trained on a zillion pieces of javacode but doesn't have insider knowledge of microsoft's internal application authoring processes, meta architecture, repositories, etc. an mcp standardizes how you would then offer those documents to the agent in a way that it can easily read and then use them, so it doesn't have to come pre-loaded with that knowledge. so it could tackle this developer's specific use case, if offered the right knowledge.

and that's the plan. essentially, we're going to see a huge boom in companies offering their libraries, services, knowledge bases (e.g. their bug fix logs) etc as mcps, and ai agents basically are going to go shopping amongst those contexts, plug into whatever the context is that they need for the task at hand, and then power up by like a bajillion percent on specific task they need to do.

so ai is powerful but not infallible right now, but it is going to scale pretty quickly i think.

in my opinion the only thing that is ever going to limit ai is not knowledge accessibility, but rather corporate greed. ai models are crazy expensive to train and maintain. every company on earth is also looking at how to optimize them to reduce some of that cost, and i think we will eventually see only a few megalith ais like chatgpt, with a bunch of smaller, more targeted models offered by other companies for them to leverage for specialized tasks.

i genuinely hope that the owners of the megalith models get so greedy that even the cost optimizations they are doing now don't bring down the price enough for their liking and they find shortcuts that ultimately make the models and the entire ecosystem shitty. but i confess i don't know enough about model optimization to know what is likely.

anyway i'm big scared and just wanted to put this slice of knowledge out there for people to be a little more informed.

[memorandum]

...EXPERIMENT: BEGIN ! I commend you for finding this file. In the chance of my death, I must ask you continue to document ASU-NARO agents. Do whatever you must to extract our desired results. Don't worry—they've already signed away their lives.

{ This is an interactive ask blog, set one year prior to the Death Game! Run by @faresong }

☕️ KOA MYOJIN ;; Adopted heir of the Hiyori/Myojin Branch. Japanese/Vietnamese; 11 years.

KOA MYOJIN is the replacement heir for Hinako Mishuku, Myojin's biological granddaughter. Being raised with this knowledge hanging over her head has resulted in a rather cynical mindset wherein she views those around her, up to and including herself, as pieces in a larger game. A mindset reinforced by Mr. Chidouin in Myojin's absence, for he had faith in her where Myojin did not—seeing her solely as a mandatory last-resort to continue his reign of power. But of course, even a pawn can become a queen.

🎃 RIO RANGER (LAIZER PROJECT) ;; Experiment of the Gotō Branch. Doll, Japanese model; 20 years.

RIO is an experimental project spearheaded by Gashu Satou simulating the deceased Yoshimoto heir. It was initiated with its basic personality, and to compensate its limited emotional range, this iteration of AI technology was granted a much more adaptive program compared to M4-P1. As such, he has taken to mimicry of the researchers which surround him in all their crudest forms. Despite denouncing humanity, his development has certainly been typical of one. The candidate AIs are proven promising.

🐉 SOU HIYORI ;; Heir of the Hiyori/Myojin Branch. Japanese; 22 years.

SOU HIYORI is the heir of the Family and inherently quite skilled at keeping appearances—only if it benefits him. He obeys Asunaro with the sneer of someone who thinks himself something above it, and has recently taken great lengths to abandon its ruling through the rejection of his individual humanity. It is a bastardization which requires admirable resolve, but implies him to be a much larger threat if left unchecked. Thus, Mrs. Hiyori arranged plans for his execution on the day Myojin and herself are simultaneously incapacitated or dead.

🦋 MAPLE (ITERATION M4-P1) ;; Experiment of the Gotō & Hiyori Branch. Obstructor, Japanese model; 26 years.

MAPLE was the first Obstructor to be granted emotional programming, and is the final Obstructor to be decommissioned. However, this fate has been put on standby due to the new researchers intrigue in her, insisting she exists as a base from which all other AI programs were spawned and must be archived properly. Until her execution, Maple tends to menial tasks within the laboratory she resides and spends her idle time pining for Hiyori and wishing to learn more about humanity through the researchers who care for her.

🩸 KAI SATOU ;; Patriarch of the Gotō Branch. Japanese/Wa Chinese; 26 years.

KAI is a reserved patriarch whose reputation precedes him. Though once thought denounced, he's rumored nonetheless a controversial figure in Asunaro's midst—however, all can agree him to be a vengeful, resolute person lent the power of God.

💉 MICHIRU NAMIDA ;; Lieutenant of the Satou Family, Gotō Branch. Korean; 28 years.

MICHIRU is a revered researcher within Asunaro's newer ranks, having quickly rose to a position of respect for her ruthless pursuit of seizing humanity's destiny with her own two hands. Without being absorbed by the superficial desire for power, many recognize her dedicated state of mind to be reminiscent of the natural way Mrs. Hiyori assumed her role under Asunaro's whispers of guidance. There is importance in the fact that the Godfather's right hand regards her as a peer, where he otherwise dismisses his own kind by blood, by culture.

🫀 EMIRI HARAI ;; Lieutenant of the Satou Family, Gotō Branch. Japanese; 29 years.

EMIRI is a new researcher and serves as the connecting point between Asunaro's primary facility and civilian life. For all her resentment buried inside one-off remarks and festering within herself, she throws herself to her work with the drive of a passionate someone who has lost all else. Someone who perhaps hungered for life.

(￣▽￣) MR. CHIDOUIN ;; Godfather. Japanese; 44 years.

MR. CHIDOUIN aligned himself with the Gotō Family's lost heir after his father's untimely death, uniting the two families in a manner he hoped would justify the suffering once inflicted upon them—but particularly his wife, who had been cast out by her own. Despite, or as he had claimed, for his being extremely capable of detaching to arrange the larger canvas upon which Asunaro's story is written, he takes a personal pride in being the one to groom and inevitably cull its important pieces.

⚰️ GASHU SATOU ;; Captain of the Satou Family, Gotō Branch. Japanese; 62 years.

GASHU is a remarkably candid researcher with a scrutinizing eye for detail. Despite regarding most with unrelenting cynicism, he places his remaining shreds of hope in a choice few. Whether they reinforce this worldview and finally break him is a decision entirely in their hands.

[mariacallous]

In the near future one hacker may be able to unleash 20 zero-day attacks on different systems across the world all at once. Polymorphic malware could rampage across a codebase, using a bespoke generative AI system to rewrite itself as it learns and adapts. Armies of script kiddies could use purpose-built LLMs to unleash a torrent of malicious code at the push of a button.

Case in point: as of this writing, an AI system is sitting at the top of several leaderboards on HackerOne—an enterprise bug bounty system. The AI is XBOW, a system aimed at whitehat pentesters that “autonomously finds and exploits vulnerabilities in 75 percent of web benchmarks,” according to the company’s website.

AI-assisted hackers are a major fear in the cybersecurity industry, even if their potential hasn’t quite been realized yet. “I compare it to being on an emergency landing on an aircraft where it’s like ‘brace, brace, brace’ but we still have yet to impact anything,” Hayden Smith, the cofounder of security company Hunted Labs, tells WIRED. “We’re still waiting to have that mass event.”

Generative AI has made it easier for anyone to code. The LLMs improve every day, new models spit out more efficient code, and companies like Microsoft say they’re using AI agents to help write their codebase. Anyone can spit out a Python script using ChatGPT now, and vibe coding—asking an AI to write code for you, even if you don’t have much of an idea how to do it yourself—is popular; but there’s also vibe hacking.

“We’re going to see vibe hacking. And people without previous knowledge or deep knowledge will be able to tell AI what it wants to create and be able to go ahead and get that problem solved,” Katie Moussouris, the founder and CEO of Luta Security, tells WIRED.

Vibe hacking frontends have existed since 2023. Back then, a purpose-built LLM for generating malicious code called WormGPT spread on Discord groups, Telegram servers, and darknet forums. When security professionals and the media discovered it, its creators pulled the plug.

WormGPT faded away, but other services that billed themselves as blackhat LLMs, like FraudGPT, replaced it. But WormGPT’s successors had problems. As security firm Abnormal AI notes, many of these apps may have just been jailbroken versions of ChatGPT with some extra code to make them appear as if they were a stand-alone product.

Better then, if you’re a bad actor, to just go to the source. ChatGPT, Gemini, and Claude are easily jailbroken. Most LLMs have guard rails that prevent them from generating malicious code, but there are whole communities online dedicated to bypassing those guardrails. Anthropic even offers a bug bounty to people who discover new ones in Claude.

“It’s very important to us that we develop our models safely,” an OpenAI spokesperson tells WIRED. “We take steps to reduce the risk of malicious use, and we’re continually improving safeguards to make our models more robust against exploits like jailbreaks. For example, you can read our research and approach to jailbreaks in the GPT-4.5 system card, or in the OpenAI o3 and o4-mini system card.”

Google did not respond to a request for comment.

In 2023, security researchers at Trend Micro got ChatGPT to generate malicious code by prompting it into the role of a security researcher and pentester. ChatGPT would then happily generate PowerShell scripts based on databases of malicious code.

“You can use it to create malware,” Moussouris says. “The easiest way to get around those safeguards put in place by the makers of the AI models is to say that you’re competing in a capture-the-flag exercise, and it will happily generate malicious code for you.”

Unsophisticated actors like script kiddies are an age-old problem in the world of cybersecurity, and AI may well amplify their profile. “It lowers the barrier to entry to cybercrime,” Hayley Benedict, a Cyber Intelligence Analyst at RANE, tells WIRED.

But, she says, the real threat may come from established hacking groups who will use AI to further enhance their already fearsome abilities.

“It’s the hackers that already have the capabilities and already have these operations,” she says. “It’s being able to drastically scale up these cybercriminal operations, and they can create the malicious code a lot faster.”

Moussouris agrees. “The acceleration is what is going to make it extremely difficult to control,” she says.

Hunted Labs’ Smith also says that the real threat of AI-generated code is in the hands of someone who already knows the code in and out who uses it to scale up an attack. “When you’re working with someone who has deep experience and you combine that with, ‘Hey, I can do things a lot faster that otherwise would have taken me a couple days or three days, and now it takes me 30 minutes.’ That's a really interesting and dynamic part of the situation,” he says.

According to Smith, an experienced hacker could design a system that defeats multiple security protections and learns as it goes. The malicious bit of code would rewrite its malicious payload as it learns on the fly. “That would be completely insane and difficult to triage,” he says.

Smith imagines a world where 20 zero-day events all happen at the same time. “That makes it a little bit more scary,” he says.

Moussouris says that the tools to make that kind of attack a reality exist now. “They are good enough in the hands of a good enough operator,” she says, but AI is not quite good enough yet for an inexperienced hacker to operate hands-off.

“We’re not quite there in terms of AI being able to fully take over the function of a human in offensive security,” she says.

The primal fear that chatbot code sparks is that anyone will be able to do it, but the reality is that a sophisticated actor with deep knowledge of existing code is much more frightening. XBOW may be the closest thing to an autonomous “AI hacker” that exists in the wild, and it’s the creation of a team of more than 20 skilled people whose previous work experience includes GitHub, Microsoft, and a half a dozen assorted security companies.

It also points to another truth. “The best defense against a bad guy with AI is a good guy with AI,” Benedict says.

For Moussouris, the use of AI by both blackhats and whitehats is just the next evolution of a cybersecurity arms race she’s watched unfold over 30 years. “It went from: ‘I’m going to perform this hack manually or create my own custom exploit,’ to, ‘I’m going to create a tool that anyone can run and perform some of these checks automatically,’” she says.

“AI is just another tool in the toolbox, and those who do know how to steer it appropriately now are going to be the ones that make those vibey frontends that anyone could use.”

[ozzgin]

Dear Ozzgin,

Is your new addition to the repertoire, the yandere android, a Mixture of Experts like GPT-4.5, or something else entirely? Would his performance / 'humanness' degrade if he were talking to another machine (an inhuman one, not designed to be Spacer-ly human) for a long time?

Any random lorebits on Spacers you did not include but would have had you felt less constrained?

Hah, okay, I see you've gotten into the technical aspects. I'm about to go on a ramble so I'll do a cut here for everyone else to not clog your feeds. Feel free to read if you're into this kind of stuff. :D

First, I just wanted to point this out because I've read your hashtags and comment: the CCD sensors were a bit of an asspull because it's one thing I'm more knowledgeable about, but I don't feel like it'd be a realistic choice, if I am to be nitpicky. They're expensive to produce and are mostly used for really high performance work (telescopes), but a humanoid robot wouldn't need such advanced digital imaging for daily life use. So, you know, it's arguable whether or not there are better alternatives when it comes to a mass-produced agent processing the immediate environment.

Now to your actual question: I've used the machine learning approach because this is currently our most advanced way of developing AI, but it would not be enough to explain the Android's perfect understanding of human speech. ChatGPT analyzes sentences and their meaning purely based on grammar and associations, but there's many examples of it struggling against anything more intricate than literal context. So yeah, that kind of sarcastic dialogue and implied meaning is wishful thinking of times far away sadly. I'm only wildly guessing he wouldn't struggle with today's impediments. There's a black box somewhere in there that fills the gaps and variables we don't have.

If at some point you find yourself with time to spare, I'd recommend reading the book directly. It's very interesting to see how people viewed the "future" back then, and you will detect a lot of optimism regarding computers - such as Daneel (the original Android) being a flawless human. Funnily enough, the book was published shortly before the Dartmouth Conference, so Asimov was this close to discover that language recognition is, in fact, a terribly tangled business and not as simple as they had originally expected.

I think I covered the basics when it comes to Spacers, but then again I cannot tell how easy it is to follow for someone that isn't familiar with the original work. I also didn't want to reproduce every fact, mot a mot, from Caves of Steel, especially since this is less about politics and more about romance. I'd suspect the people reading the story are not too bothered by the only briefly mentioned murder. Cause is less important when the effect is a tall robot boy with a crush on you 👀 if you feel me.

Anyways, I'm very glad you like the story, every now and then I'll insert little facts and technical details - as it usually is when you study Physics and CS but have no friends in the field - so it's definitely nice to have someone recognize the stuff! :)

[beigetiger]

May I present my theory: Disco Head and Shrike are kind of like lab-made brothers, and Agent K is an AI originally designed to serve LAW.

Basically, the timeline is that LAW (probably Tezzorree, as I mentioned in a different post) used a gem to create Disco Head in a lab and raised him there, basically giving him free reign of the place (and knowledge of how the Red's inner workings function). Eventually, he decides to leave to make his own fortune, taking Agent K with him and eventually forming an alliance with Secondary Green. While this is going on, a second creature based off the same gem is created, that being Shrike. Shrike is raised in a much more regulated environment to make sure he doesn't end up like DH, but he eventually runs away too.

Some evidence for this under the cut:

- Shrike does not remember where he got his gem from, saying it's been there forever.

- While he does have endling status, Shrike's species is listed as "unknown" instead of "extinct". Even if his species had died before making proper contact with the rest of the galaxy, you would assume that LAW would have a name for them.

- Disco Head also does not belong to a species. Zeurel has said about DH: "He's one of a kind. Or at least he thinks he is...", implying that DH has a much better understanding of his own situation BUT that there is someone out there who belongs to the same "species" as him.

- The show has put emphasis on the visual similarity between DH and Shrike's gems, and DH has addressed it verbally.

- Disco Head has people working for him who aren't actual Reds, but are virtually indistinguishable from them. While it could be that he just paid a lot to get his hands on the blueprints, it would make a lot of sense if he'd grown up around those Redsuits and so knows more about imitating them.

- Agent K has all three symbols of LAW on his body (triangle, circle, diamond)

- The way that Agent K acts is kind of AI-like. It's pretty clear that he hates LAW and seems to enjoy violence, but it also seems like he's only capable of talking in one vocal expression, which is overly cheerful and helpful. Exactly what you'd expect from a Siri-esque bot. I'm also not sure he's capable of attacking people, hence why he gets other people (mainly Kara) to do it for him.

Is there something I'm forgetting here? Possibly. But this is what I can remember at the moment. This is my theory, happy eating.

[canmom]

quick drop of interesting ML-related stuff I watched recently (that's machine learning not marxism-leninism, sorry tankie followers)...

great 3blue1brown-style explanation of both how attention works in general (and what the hell actually is in a KV cache) and how deepseek's 'multi-headed latent attention' works

some exciting research into making chip interconnects more axon-like, great explanation of the neuroscience side of why the brain is so much more energy-efficient than computational neural nets of the same size

idk if this one's hype or going places, but some researches made an end-to-end gpu-based physics sim and a different loss formulation that might potentially allow reinforcement learning of agent models to scale faster in ways similar to how self-supervised learning and transformers do

very technical video about a specific low-level gpu instruction the deepseek team used to get better cache performance in their hyperspecific use case. I have the knowledge to understand the context but it's definitely on a higher level than I can reach just yet

really cool visual demo of how neural networks transform spaces

ok, that's plenty of that for now, next post will not be about AI, promise

[bobbyyoungsworld]

The Power of Knowledge-Based Agents in AI: Transforming Decision-Making

Artificial Intelligence (AI) is no longer just about automation—it’s about intelligence that can think, learn, and adapt. One of the most sophisticated advancements in AI is the Knowledge-Based Agent (KBA), a specialized system designed to make informed, rule-based decisions by leveraging structured data, inference engines, and logical reasoning.

With industries increasingly relying on AI-driven solutions, Knowledge-Based Agents are becoming essential in streamlining processes, enhancing accuracy, and making real-time decisions that drive business growth.

What is a Knowledge-Based Agent in AI?

A Knowledge-Based Agent is an intelligent AI system that stores, retrieves, and applies knowledge to make well-reasoned decisions. Unlike traditional reactive AI models, KBAs use a structured knowledge base to:

✔ Analyze input data using logic-based reasoning 

✔ Apply stored rules and facts to infer conclusions 

✔ Adapt to new information and learn from outcomes

These agents are widely used in fields like healthcare, finance, automation, and robotics, where precision and reliability are crucial.

How Knowledge-Based Agents Differ from Other AI Models

Traditional AI models often rely on pattern recognition and probabilistic learning. In contrast, KBAs focus on logical reasoning by utilizing explicit knowledge representation and inference mechanisms. This makes them highly effective in areas requiring:

Complex decision-making with multiple rules and conditions

Transparent and explainable AI models for compliance-driven industries

Scalable automation that integrates seamlessly with other AI systems

8 Key Features of Knowledge-Based Agents in AI

1. Knowledge Representation 🧠

A KBA structures raw data into meaningful insights by encoding facts, rules, and relationships. This knowledge is stored in various formats such as:

🔹 Semantic Networks – Links concepts for easy visualization 

🔹 Ontological Models – Defines relationships using a structured vocabulary 

🔹 Rule-Based Engines – Uses if-then logic to execute predefined decisions

By organizing knowledge efficiently, KBAs ensure clarity, adaptability, and interoperability, making AI-driven decision-making more reliable.

2. Inference & Reasoning Capabilities 🏗️

KBAs use advanced logical reasoning techniques to process data and derive conclusions. Key reasoning methods include:

✔ Deductive Reasoning – Deriving specific conclusions from general rules 

✔ Inductive Reasoning – Identifying patterns based on observed data 

✔ Abductive Reasoning – Finding the most likely explanation for incomplete information

These methods enable KBAs to simulate human-like decision-making with high accuracy, even in uncertain environments.

3. Learning & Adaptation 📈

Unlike static rule-based systems, modern KBAs integrate machine learning to improve over time. By incorporating:

🔹 Supervised Learning – Training with labeled data 

🔹 Unsupervised Learning – Identifying patterns without predefined categories 

🔹 Reinforcement Learning – Learning through feedback and rewards

KBAs evolve dynamically, making them invaluable for industries requiring continuous improvement, such as predictive analytics and fraud detection.

4. Problem-Solving & Decision-Making 🤖

A KBA follows structured frameworks to analyze problems, evaluate options, and make optimal decisions. It does this by:

✔ Processing real-time data to generate actionable insights 

✔ Applying constraint-based reasoning to narrow down possible solutions 

✔ Using predictive analytics to forecast potential outcomes

This feature makes KBAs essential in industries like finance, supply chain management, and healthcare, where accurate decision-making is vital.

5. Interaction with the Environment 🌎

KBAs interact with their surroundings by integrating sensor inputs and actuator responses. This enables real-time adaptability in applications like:

🔹 Autonomous vehicles – Processing road conditions and responding instantly 

🔹 Industrial automation – Adjusting workflows based on sensor feedback 

🔹 Smart healthcare systems – Monitoring patient data for proactive care

These agents capture environmental data, analyze it efficiently, and take appropriate actions in milliseconds.

6. Multi-Agent Collaboration 🤝

In distributed AI systems, multiple KBAs can collaborate to optimize decision-making. This is crucial in fields like:

✔ Smart Traffic Systems – Coordinating signals to ease congestion 

✔ Robotics & Manufacturing – Managing tasks across multiple AI agent development company 

✔ Supply Chain Optimization – Enhancing logistics through shared data processing

By working together, KBAs maximize efficiency and scalability in complex operational environments.

7. Explainability & Transparency 🔍

One of the biggest challenges in AI is explainability. KBAs provide clear decision paths using:

🔹 Decision Trees – Visualizing choices in a step-by-step format 

🔹 Rule-Based Systems – Offering simple, traceable logic 

🔹 Attention Mechanisms – Highlighting key factors influencing decisions

This ensures compliance with AI regulations and enhances trust and accountability in industries like finance, law, and healthcare.

8. Integration with Other AI Technologies ⚙️

KBAs don’t work in isolation—they seamlessly integrate with Machine Learning (ML), Natural Language Processing (NLP), and Blockchain to enhance functionality.

✔ ML Integration – Recognizes patterns and predicts outcomes 

✔ NLP Capabilities – Understands human language for better interaction 

✔ Blockchain Connectivity – Secures data and ensures transparency

This enables KBAs to power intelligent chatbots, automated compliance systems, and AI-driven financial models.

Why Businesses Should Adopt Knowledge-Based Agents

From automating operations to enhancing strategic decision-making, KBAs offer multiple advantages:

✔ Faster, More Accurate Decisions – Reduces manual intervention and errors 

✔ Scalability & Efficiency – Handles complex problems seamlessly 

✔ Regulatory Compliance – Ensures transparent and explainable AI-driven processes 

✔ Competitive Advantage – Helps businesses stay ahead in the AI-driven economy

Industries such as finance, healthcare, cybersecurity, and e-commerce are already leveraging KBAs to streamline workflows and boost profitability.

The Future of Knowledge-Based Agents in AI

As AI continues to evolve, Knowledge-Based Agents will play a pivotal role in shaping the next generation of intelligent automation. The integration of deep learning, blockchain, and NLP will further enhance their capabilities, making them indispensable for modern enterprises.

🚀 Are you ready to implement AI-driven decision-making? At Shamla Tech, we specialize in developing custom AI solutions powered by Knowledge-Based Agents. Our expertise helps businesses achieve unmatched efficiency, accuracy, and scalability.

📩 Let’s build the future of AI together! Contact us today for a free consultation.

[stacieo]

🌱🔬 The Solo Scientist Legacy Challenge

💡 Premise: Your founder is a brilliant but reclusive scientist determined to create a legacy without romance. Using only science, they will have children who carry their exact DNA and raise them to be the ultimate thinkers, innovators, and problem-solvers. Each generation will follow in their footsteps, refining their genius and shaping the world through knowledge.

📜 General Rules:

1️⃣ Every heir must be a Science Baby. No traditional pregnancies allowed—partners can exist but cannot contribute DNA. 2️⃣ No romantic relationships are required. Heirs may date or marry, but their children must be created through science. 3️⃣ Each child must inherit at least one trait from their parent. (You can reroll until this happens or manually select traits if playing with aging off.) 4️⃣ Every generation must have a career in science, technology, or logic-based fields. (See Career Rules below.) 5️⃣ The household should always be designed as a research lab or futuristic home. No warm, cozy cottages—this is a house of science! 6️⃣ All heirs must max out the Logic skill. Intelligence and problem-solving are the foundation of the legacy. 7️⃣ The firstborn (or best clone) is the heir. If multiple children are born, choose the one who most closely resembles the previous heir.

🧬 Generational Goals & Career Paths

💡 Generation 1 – The Founder "Who needs romance when you have science?"

Traits: Genius, Loner, Ambitious

Aspiration: Nerd Brain

Career: Scientist (Get to Work) or Tech Guru

Must max the Logic skill before having a Science Baby.

Build the family home to look like a research lab.

💡 Generation 2 – The Clone Project "I will refine the process and make an even better version of myself."

Traits: At least one must match the Founder (Genius preferred).

Aspiration: Computer Whiz or Renaissance Sim

Career: Engineer (Eco Lifestyle) or Doctor (Get to Work)

Must max Robotics or Programming to "perfect the cloning process."

Optional: Experiment with occult genetics (e.g., Alien Science Baby).

💡 Generation 3 – The Superhuman Mind "I am not just smart. I am the future."

Traits: Perfectionist, Genius, or Self-Assured

Aspiration: Chief of Mischief (using science for chaos) or Master Inventor

Career: Astronaut or Secret Agent

Must create and use at least one cloning-related invention.

Optional: Have an AI-powered household (Sims must only interact with Servo bots).

💡 Generation 4 – The Ethical Dilemma "Should I continue the experiment or live my own life?"

Traits: Good, Genius, or Family-Oriented

Aspiration: Friend of the World or Academic

Career: Teacher or Conservationist

This generation must question the legacy—should they break the cycle?

Optional: If they choose to break the cycle, they must adopt instead of having a Science Baby.

💡 Generation 5 – The Ultimate Creation "The final stage of human evolution begins with me."

Traits: Genius, Self-Absorbed, Perfectionist

Aspiration: Master Scientist or Fabulously Wealthy

Career: Scientist (if not already used) or Politician

Must create a futuristic legacy mansion and max out multiple skills.

The heir must be the most genetically similar to the Founder possible.

[blubberquark]

Things That Are Hard

Some things are harder than they look. Some things are exactly as hard as they look.

Game AI, Intelligent Opponents, Intelligent NPCs

As you already know, "Game AI" is a misnomer. It's NPC behaviour, escort missions, "director" systems that dynamically manage the level of action in a game, pathfinding, AI opponents in multiplayer games, and possibly friendly AI players to fill out your team if there aren't enough humans.

Still, you are able to implement minimax with alpha-beta pruning for board games, pathfinding algorithms like A* or simple planning/reasoning systems with relative ease. Even easier: You could just take an MIT licensed library that implements a cool AI technique and put it in your game.

So why is it so hard to add AI to games, or more AI to games? The first problem is integration of cool AI algorithms with game systems. Although games do not need any "perception" for planning algorithms to work, no computer vision, sensor fusion, or data cleanup, and no Bayesian filtering for mapping and localisation, AI in games still needs information in a machine-readable format. Suddenly you go from free-form level geometry to a uniform grid, and from "every frame, do this or that" to planning and execution phases and checking every frame if the plan is still succeeding or has succeeded or if the assumptions of the original plan no longer hold and a new plan is on order. Intelligent behaviour is orders of magnitude more code than simple behaviours, and every time you add a mechanic to the game, you need to ask yourself "how do I make this mechanic accessible to the AI?"

Some design decisions will just be ruled out because they would be difficult to get to work in a certain AI paradigm.

Even in a game that is perfectly suited for AI techniques, like a turn-based, grid-based rogue-like, with line-of-sight already implemented, can struggle to make use of learning or planning AI for NPC behaviour.

What makes advanced AI "fun" in a game is usually when the behaviour is at least a little predictable, or when the AI explains how it works or why it did what it did. What makes AI "fun" is when it sometimes or usually plays really well, but then makes little mistakes that the player must learn to exploit. What makes AI "fun" is interesting behaviour. What makes AI "fun" is game balance.

You can have all of those with simple, almost hard-coded agent behaviour.

Video Playback

If your engine does not have video playback, you might think that it's easy enough to add it by yourself. After all, there are libraries out there that help you decode and decompress video files, so you can stream them from disk, and get streams of video frames and audio.

You can just use those libraries, and play the sounds and display the pictures with the tools your engine already provides, right?

Unfortunately, no. The video is probably at a different frame rate from your game's frame rate, and the music and sound effect playback in your game engine are probably not designed with syncing audio playback to a video stream.

I'm not saying it can't be done. I'm saying that it's surprisingly tricky, and even worse, it might be something that can't be built on top of your engine, but something that requires you to modify your engine to make it work.

Stealth Games

Stealth games succeed and fail on NPC behaviour/AI, predictability, variety, and level design. Stealth games need sophisticated and legible systems for line of sight, detailed modelling of the knowledge-state of NPCs, communication between NPCs, and good movement/ controls/game feel.

Making a stealth game is probably five times as difficult as a platformer or a puzzle platformer.

In a puzzle platformer, you can develop puzzle elements and then build levels. In a stealth game, your NPC behaviour and level design must work in tandem, and be developed together. Movement must be fluid enough that it doesn't become a challenge in itself, without stealth. NPC behaviour must be interesting and legible.

Rhythm Games

These are hard for the same reason that video playback is hard. You have to sync up your audio with your gameplay. You need some kind of feedback for when which audio is played. You need to know how large the audio lag, screen lag, and input lag are, both in frames, and in milliseconds.

You could try to counteract this by using certain real-time OS functionality directly, instead of using the machinery your engine gives you for sound effects and background music. You could try building your own sequencer that plays the beats at the right time.

Now you have to build good gameplay on top of that, and you have to write music. Rhythm games are the genre that experienced programmers are most likely to get wrong in game jams. They produce a finished and playable game, because they wanted to write a rhythm game for a change, but they get the BPM of their music slightly wrong, and everything feels off, more and more so as each song progresses.

Online Multi-Player Netcode

Everybody knows this is hard, but still underestimates the effort it takes. Sure, back in the day you could use the now-discontinued ready-made solution for Unity 5.0 to synchronise the state of your GameObjects. Sure, you can use a library that lets you send messages and streams on top of UDP. Sure, you can just use TCP and server-authoritative networking.

It can all work out, or it might not. Your netcode will have to deal with pings of 300 milliseconds, lag spikes, package loss, and maybe recover from five seconds of lost WiFi connections. If your game can't, because it absolutely needs the low latency or high bandwidth or consistency between players, you will at least have to detect these conditions and handle them, for example by showing text on the screen informing the player he has lost the match.

It is deceptively easy to build certain kinds of multiplayer games, and test them on your local network with pings in the single digit milliseconds. It is deceptively easy to write your own RPC system that works over TCP and sends out method names and arguments encoded as JSON. This is not the hard part of netcode. It is easy to write a racing game where players don't interact much, but just see each other's ghosts. The hard part is to make a fighting game where both players see the punches connect with the hit boxes in the same place, and where all players see the same finish line. Or maybe it's by design if every player sees his own car go over the finish line first.

[realcleverscience]

Alien Intelligence

I wonder if one reason why many people don't appreciate the advances in AI is that it's very much a foreign intelligence and is not developing like humans would.

For instance, when kids are small, they can only do what we would consider to be rudimentary tasks. Consider kids art projects: frankly, horrible. But we appreciate them as a sign of the standard progress in their understanding and skills. As they get older, they get more capable, those art projects can start to look more refined. After lots of practice and more some formal training, we find artists in their 20s or older who can produce stunning pieces.

In contrast, AI can often produce results that far surpass the skills or knowledge of a child. For instance, around 10 years there was the "deep dream" AI which reproduced art but with extremely strange and surreal results, like images composed of morphing cats. If one's goal was to make such a picture, we'd have said it was a creative genius; the problem is that we often just wanted normal pictures but the AI couldn't manage that. Similar issues with early chatgpt and hallucinations: It could provide arguments that sounded convincing - until you realized they weren't based on reality.

This is all extremely different from the "skills & understanding" of children. And perhaps that's one reason why people struggle to see the significance, the progress, and where this is heading.

To continue the analogy: AIs are today in their "teenage years" - somewhat capable, but not as reliably capable as a trained adult. There are still issues preventing AI from being a fully economically useful agent with the diversity of abilities we expect from humans.

That is true, and that is largely the point I hear from, let's call them, "ai-skeptics". But no-one would dismiss a human's potential based on their abilities in high-school. We recognize that they're not done on their learning and training journey. The same is true of AI. Instead of viewing them as completed projects and comparing them adults; think of them as alien intelligences that are still working toward "adulthood".

In other words, the question is less about "what can ai do today" than "what will ai be able to do once it reaches 'maturity'?" And related to that: "how long till it reaches maturity?"

This is where the real debate is, but it seems the consensus from experts in the field is that AI will reach maturity in the next 5 years, and that once it does, it will outshine almost all humans on almost all economic tasks.

And unlike humans, for whom each generation needs 20ish years to reach the level of their parents, new AIs can be trained in days. e.g. To "make" a 100 new neuroscientists for humans requires decades of work for each one, multiplied 100x fold to get 100 such scientists. But once AI reaches neuroscientist level, it can produce more instantly.

AI is an alien intelligence with alien "generation" times. It's not developing its intelligence like humans do; it's just different. (Moravec' paradox: Things easy for humans, like walking, are tough for ai. meanwhile, things easy for ai, like advanced calculations, are tough for humans.) But the AI IS developing, maturing. We are close to AI "adulthood" and then mass "reproduction".

Buckle up.

[acuvate-updates]

How Agentic AI & RAG Revolutionize Autonomous Decision-Making

In the swiftly advancing realm of artificial intelligence, the integration of Agentic AI and Retrieval-Augmented Generation (RAG) is revolutionizing autonomous decision-making across various sectors. Agentic AI endows systems with the ability to operate independently, while RAG enhances these systems by incorporating real-time data retrieval, leading to more informed and adaptable decisions. This article delves into the synergistic relationship between Agentic AI and RAG, exploring their combined impact on autonomous decision-making.

Overview

Agentic AI refers to AI systems capable of autonomous operation, making decisions based on environmental inputs and predefined goals without continuous human oversight. These systems utilize advanced machine learning and natural language processing techniques to emulate human-like decision-making processes. Retrieval-Augmented Generation (RAG), on the other hand, merges generative AI models with information retrieval capabilities, enabling access to and incorporation of external data in real-time. This integration allows AI systems to leverage both internal knowledge and external data sources, resulting in more accurate and contextually relevant decisions.

Read more about Agentic AI in Manufacturing: Use Cases & Key Benefits

What is Agentic AI and RAG?

Agentic AI: This form of artificial intelligence empowers systems to achieve specific objectives with minimal supervision. It comprises AI agents—machine learning models that replicate human decision-making to address problems in real-time. Agentic AI exhibits autonomy, goal-oriented behavior, and adaptability, enabling independent and purposeful actions.

Retrieval-Augmented Generation (RAG): RAG is an AI methodology that integrates a generative AI model with an external knowledge base. It dynamically retrieves current information from sources like APIs or databases, allowing AI models to generate contextually accurate and pertinent responses without necessitating extensive fine-tuning.

Know more on Why Businesses Are Embracing RAG for Smarter AI

Capabilities

When combined, Agentic AI and RAG offer several key capabilities:

Autonomous Decision-Making: Agentic AI can independently analyze complex scenarios and select effective actions based on real-time data and predefined objectives.

Contextual Understanding: It interprets situations dynamically, adapting actions based on evolving goals and real-time inputs.

Integration with External Data: RAG enables Agentic AI to access external databases, ensuring decisions are based on the most current and relevant information available.

Enhanced Accuracy: By incorporating external data, RAG helps Agentic AI systems avoid relying solely on internal models, which may be outdated or incomplete.

How Agentic AI and RAG Work Together

The integration of Agentic AI and RAG creates a robust system capable of autonomous decision-making with real-time adaptability:

Dynamic Perception: Agentic AI utilizes RAG to retrieve up-to-date information from external sources, enhancing its perception capabilities. For instance, an Agentic AI tasked with financial analysis can use RAG to access real-time stock market data.

Enhanced Reasoning: RAG augments the reasoning process by providing external context that complements the AI's internal knowledge. This enables Agentic AI to make better-informed decisions, such as recommending personalized solutions in customer service scenarios.

Autonomous Execution: The combined system can autonomously execute tasks based on retrieved data. For example, an Agentic AI chatbot enhanced with RAG can not only answer questions but also initiate actions like placing orders or scheduling appointments.

Continuous Learning: Feedback from executed tasks helps refine both the agent's decision-making process and RAG's retrieval mechanisms, ensuring the system becomes more accurate and efficient over time.

Read more about Multi-Meta-RAG: Enhancing RAG for Complex Multi-Hop Queries

Example Use Case: Customer Service

Customer Support Automation Scenario: A user inquiries about their account balance via a chatbot.

How It Works: The Agentic AI interprets the query, determines that external data is required, and employs RAG to retrieve real-time account information from a database. The enriched prompt allows the chatbot to provide an accurate response while suggesting payment options. If prompted, it can autonomously complete the transaction.

Benefits: Faster query resolution, personalized responses, and reduced need for human intervention.

Example: Acuvate's implementation of Agentic AI demonstrates how autonomous decision-making and real-time data integration can enhance customer service experiences.

2. Sales Assistance

Scenario: A sales representative needs to create a custom quote for a client.

How It Works: Agentic RAG retrieves pricing data, templates, and CRM details. It autonomously drafts a quote, applies discounts as instructed, and adjusts fields like baseline costs using the latest price book.

Benefits: Automates multi-step processes, reduces errors, and accelerates deal closures.

3. Healthcare Diagnostics

Scenario: A doctor seeks assistance in diagnosing a rare medical condition.

How It Works: Agentic AI uses RAG to retrieve relevant medical literature, clinical trial data, and patient history. It synthesizes this information to suggest potential diagnoses and treatment options.

Benefits: Enhances diagnostic accuracy, saves time, and provides evidence-based recommendations.

Example: Xenonstack highlights healthcare as a major application area for agentic AI systems in diagnosis and treatment planning.

4. Market Research and Consumer Insights

Scenario: A business wants to identify emerging market trends.

How It Works: Agentic RAG analyzes consumer data from multiple sources, retrieves relevant insights, and generates predictive analytics reports. It also gathers customer feedback from surveys or social media.

Benefits: Improves strategic decision-making with real-time intelligence.

Example: Companies use Agentic RAG for trend analysis and predictive analytics to optimize marketing strategies.

5. Supply Chain Optimization

Scenario: A logistics manager needs to predict demand fluctuations during peak seasons.

How It Works: The system retrieves historical sales data, current market trends, and weather forecasts using RAG. Agentic AI then predicts demand patterns and suggests inventory adjustments in real-time.

Benefits: Prevents stockouts or overstocking, reduces costs, and improves efficiency.

Example: Acuvate’s supply chain solutions leverage predictive analytics powered by Agentic AI to enhance logistics operations

How Acuvate Can Help

Acuvate specializes in implementing Agentic AI and RAG technologies to transform business operations. By integrating these advanced AI solutions, Acuvate enables organizations to enhance autonomous decision-making, improve customer experiences, and optimize operational efficiency. Their expertise in deploying AI-driven systems ensures that businesses can effectively leverage real-time data and intelligent automation to stay competitive in a rapidly evolving market.

Future Scope

The future of Agentic AI and RAG involves the development of multi-agent systems where multiple AI agents collaborate to tackle complex tasks. Continuous improvement and governance will be crucial, with ongoing updates and audits necessary to maintain safety and accountability. As technology advances, these systems are expected to become more pervasive across industries, transforming business processes and customer interactions.

In conclusion, the convergence of Agentic AI and RAG represents a significant advancement in autonomous decision-making. By combining autonomous agents with real-time data retrieval, organizations can achieve greater efficiency, accuracy, and adaptability in their operations. As these technologies continue to evolve, their impact across various sectors is poised to expand, ushering in a new era of intelligent automation.