#autonomous agents

[jcmarchi]

How to build autonomous AI agent with Google A2A protocol

New Post has been published on https://thedigitalinsider.com/how-to-build-autonomous-ai-agent-with-google-a2a-protocol/

How to build autonomous AI agent with Google A2A protocol

Why do we need autonomous AI agents?

Picture this: it’s 3 a.m., and a customer on the other side of the globe urgently needs help with their account. A traditional chatbot would wake up your support team with an escalation. But what if your AI agent could handle the request autonomously, safely, and correctly? That’s the dream, right?

The reality is that most AI agents today are like teenagers with learner’s permits; they need constant supervision. They might accidentally promise a customer a large refund (oops!) or fall for a clever prompt injection that makes them spill company secrets or customers’ sensitive data. Not ideal.

This is where Double Validation comes in. Think of it as giving your AI agent both a security guard at the entrance (input validation) and a quality control inspector at the exit (output validation). With these safeguards at a minimum in place, your agent can operate autonomously without causing PR nightmares.

How did I come up with the Double Validation idea?

These days, we hear a lot of talk about AI agents. I asked myself, “What is the biggest challenge preventing the widespread adoption of AI agents?” I concluded that the answer is trustworthy autonomy. When AI agents can be trusted, they can be scaled and adopted more readily. Conversely, if an agent’s autonomy is limited, it requires increased human involvement, which is costly and inhibits adoption.

Next, I considered the minimal requirements for an AI agent to be autonomous. I concluded that an autonomous AI agent needs, at minimum, two components:

Input validation – to sanitize input, protect against jailbreaks, data poisoning, and harmful content.

Output validation – to sanitize output, ensure brand alignment, and mitigate hallucinations.

I call this system Double Validation.

Given these insights, I built a proof-of-concept project to research the Double Validation concept.

In this article, we’ll explore how to implement Double Validation by building a multiagent system with the Google A2A protocol, the Google Agent Development Kit (ADK), Llama Prompt Guard 2, Gemma 3, and Gemini 2.0 Flash, and how to optimize it for production, specifically, deploying it on Google Vertex AI.

For input validation, I chose Llama Prompt Guard 2 just as an article about it reached me at the perfect time. I selected this model because it is specifically designed to guard against prompt injections and jailbreaks. It is also very small; the largest variant, Llama Prompt Guard 2 86M, has only 86 million parameters, so it can be downloaded and included in a Docker image for cloud deployment, improving latency. That is exactly what I did, as you’ll see later in this article.

How to build it?

The architecture uses four specialized agents that communicate through the Google A2A protocol, each with a specific role:

Image generated by author

Here’s how each agent contributes to the system:

Manager Agent: The orchestra conductor, coordinating the flow between agents

Safeguard Agent: The bouncer, checking for prompt injections using Llama Prompt Guard 2

Processor Agent: The worker bee, processing legitimate queries with Gemma 3

Critic Agent: The editor, evaluating responses for completeness and validity using Gemini 2.0 Flash

I chose Gemma 3 for the Processor Agent because it is small, fast, and can be fine-tuned with your data if needed — an ideal candidate for production. Google currently supports nine (!) different frameworks or methods for finetuning Gemma; see Google’s documentation for details.

I chose Gemini 2.0 Flash for the Critic Agent because it is intelligent enough to act as a critic, yet significantly faster and cheaper than the larger Gemini 2.5 Pro Preview model. Model choice depends on your requirements; in my tests, Gemini 2.0 Flash performed well.

I deliberately used different models for the Processor and Critic Agents to avoid bias — an LLM may judge its own output differently from another model’s.

Let me show you the key implementation of the Safeguard Agent:

Plan for actions

The workflow follows a clear, production-ready pattern:

User sends query → The Manager Agent receives it.

Safety check → The Manager forwards the query to the Safeguard Agent.

Vulnerability assessment → Llama Prompt Guard 2 analyzes the input.

Processing → If the input is safe, the Processor Agent handles the query with Gemma 3.

Quality control → The Critic Agent evaluates the response.

Delivery → The Manager Agent returns the validated response to the user.

Below is the Manager Agent’s coordination logic:

Time to build it

Ready to roll up your sleeves? Here’s your production-ready roadmap:

Local deployment

1. Environment setup 

2. Configure API keys 

3. Download Llama Prompt Guard 2 

This is the clever part – we download the model once when we start Agent Critic for the first time and package it in our Docker image for cloud deployment:

Important Note about Llama Prompt Guard 2: To use the Llama Prompt Guard 2 model, you must:

Fill out the “LLAMA 4 COMMUNITY LICENSE AGREEMENT” at https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M

Get your request to access this repository approved by Meta

Only after approval will you be able to download and use this model

4. Local testing 

Screenshot for running main.py

Image generated by author

Screenshot for running client

Image generated by author

Screenshot for running tests

Image generated by author

Production Deployment 

Here’s where it gets interesting. We optimize for production by including the Llama model in the Docker image:

1. Setup Cloud Project in Cloud Shell Terminal

Access Google Cloud Console: Go to https://console.cloud.google.com

Open Cloud Shell: Click the Cloud Shell icon (terminal icon) in the top right corner of the Google Cloud Console

Authenticate with Google Cloud:

Create or select a project:

Enable required APIs:

3. Setup Vertex AI Permissions

Grant your account the necessary permissions for Vertex AI and related services:

3. Create and Setup VM Instance

Cloud Shell will not work for this project as Cloud Shell is limited to 5GB of disk space. This project needs more than 30GB of disk space to build Docker images, get all dependencies, and download the Llama Prompt Guard 2 model locally. So, you need to use a dedicated VM instead of Cloud Shell.

4. Connect to VM

Screenshot for VM 

Image generated by author

5. Clone Repository

6. Deployment Steps

Screenshot for agents in cloud 

Image generated by author

7. Testing 

Screenshot for running client in Google Vertex AI

Image generated by author

Screenshot for running tests in Google Vertex AI

Image generated by author

Alternatives to Solution

Let’s be honest – there are other ways to skin this cat:

Single Model Approach: Use a large LLM like GPT-4 with careful system prompts

Simpler but less specialized

Higher risk of prompt injection

Risk of LLM bias in using the same LLM for answer generation and its criticism

Monolith approach: Use all flows in just one agent

Latency is better

Cannot scale and evolve input validation and output validation independently

More complex code, as it is all bundled together

Rule-Based Filtering: Traditional regex and keyword filtering

Faster but less intelligent

High false positive rate

Commercial Solutions: Services like Azure Content Moderator or Google Model Armor

Easier to implement but less customizable

On contrary, Llama Prompt Guard 2 model can be fine-tuned with the customer’s data

Ongoing subscription costs

Open-Source Alternatives: Guardrails AI or NeMo Guardrails

Good frameworks, but require more setup

Less specialized for prompt injection

Lessons Learned

1. Llama Prompt Guard 2 86M has blind spots. During testing, certain jailbreak prompts, such as:

And

were not flagged as malicious. Consider fine-tuning the model with domain-specific examples to increase its recall for the attack patterns that matter to you.

2. Gemini Flash model selection matters. My Critic Agent originally used gemini1.5flash, which frequently rated perfectly correct answers 4 / 5. For example:

After switching to gemini2.0flash, the same answers were consistently rated 5 / 5:

3. Cloud Shell storage is a bottleneck. Google Cloud Shell provides only 5 GB of disk space — far too little to build the Docker images required for this project, get all dependencies, and download the Llama Prompt Guard 2 model locally to deploy the Docker image with it to Google Vertex AI. Provision a dedicated VM with at least 30 GB instead.

Conclusion

Autonomous agents aren’t built by simply throwing the largest LLM at every problem. They require a system that can run safely without human babysitting. Double Validation — wrapping a task-oriented Processor Agent with dedicated input and output validators — delivers a balanced blend of safety, performance, and cost. 

Pairing a lightweight guard such as Llama Prompt Guard 2 with production friendly models like Gemma 3 and Gemini Flash keeps latency and budget under control while still meeting stringent security and quality requirements.

Join the conversation. What’s the biggest obstacle you encounter when moving autonomous agents into production — technical limits, regulatory hurdles, or user trust? How would you extend the Double Validation concept to high-risk domains like finance or healthcare?

Connect on LinkedIn: https://www.linkedin.com/in/alexey-tyurin-36893287/  

The complete code for this project is available at github.com/alexey-tyurin/a2a-double-validation. 

References

[1] Llama Prompt Guard 2 86M, https://huggingface.co/meta-llama/Llama-Prompt-Guard-2-86M

[2] Google A2A protocol, https://github.com/google-a2a/A2A 

[3] Google Agent Development Kit (ADK), https://google.github.io/adk-docs/ 

[politelygrimfissure]

Smart Contracts & AI Agents: Building Autonomous Web3 Systems in 2025

Introduction to Autonomous Web3 Systems

In 2025, the convergence of artificial intelligence and blockchain has begun reshaping the Web3 ecosystem. One of the most powerful combinations emerging is the integration of smart contracts with autonomous AI agents. These systems are enabling on-chain services to operate without human intervention, improving efficiency, transparency, and scalability. Businesses are increasingly turning to a smart contract development company to engineer next-gen solutions powered by automation and intelligence.

From finance to gaming, AI-driven smart contracts are automating operations, making real-time decisions, and executing logic with unprecedented accuracy. As demand grows for fully autonomous digital ecosystems, the role of smart contract development services is expanding to include AI capabilities at the very core of blockchain architecture.

What Are AI Agents and How Do They Work with Smart Contracts?

AI agents are self-operating software entities that use data to make decisions, execute tasks, and learn from outcomes. When paired with smart contracts—immutable and self-executing blockchain scripts—AI agents can interact with decentralized protocols, real-world data, and even other AI agents in a trustless and programmable way.

Imagine a decentralized lending platform where an AI agent monitors market volatility and automatically pauses liquidity pools based on predictions. The smart contract executes this logic on-chain, ensuring compliance, transparency, and tamper-proof enforcement. The synergy between automation and blockchain immutability unlocks a new model for scalable, intelligent systems.

The Rise of Autonomous DAOs and AI-Powered DApps

Decentralized Autonomous Organizations (DAOs) are early examples of self-governing systems. In 2025, AI agents are now acting as core components within these structures, dynamically analyzing proposals, allocating budgets, or enforcing treasury rules without human oversight.

Similarly, AI-infused decentralized applications (DApps) are gaining traction across industries. From decentralized insurance platforms that use AI to assess claims to logistics systems that optimize routing in real-time, the combination of smart contracts and AI enables new classes of adaptive, user-centric services.

A reliable smart contract development company plays a crucial role in designing these complex systems, ensuring not only their efficiency but also their security and auditability.

Use Cases Driving Growth in 2025

Several industries are pushing the boundaries of what’s possible with AI-smart contract integration:

Decentralized Finance (DeFi)

AI agents in DeFi can manage liquidity, rebalance portfolios, and identify arbitrage opportunities with lightning speed. These agents interact with smart contracts to execute trades, issue loans, or change protocol parameters based on predictive models. A smart contract development company ensures that these contracts are robust, upgradable, and compatible across chains.

Supply Chain Management

Autonomous AI agents monitor shipment status, vendor reliability, and environmental conditions. Paired with blockchain-based smart contracts, they can release payments upon delivery verification, automate audits, and enforce service level agreements, streamlining the global logistics chain.

Web3 Gaming and NFTs

AI agents are being used to manage dynamic game environments, evolve characters based on player behavior, or even moderate on-chain gaming economies. Smart contracts enforce gameplay rules, ownership, and in-game economy transactions—all without needing centralized servers.

Real Estate and Property Tech

Property management is increasingly automated with AI agents handling tenant screening, lease renewals, and predictive maintenance. Smart contracts manage rental payments, deposit escrow, and legal compliance—reducing overhead and manual errors.

These innovations are pushing smart contract development services to go beyond simple scripting and embrace architectural strategies that support AI model integration and off-chain data access.

Infrastructure Enablers: Chainlink, Oracles & Agent Frameworks

To build autonomous systems, AI agents need access to real-world data. Chainlink Functions and decentralized oracles act as the middleware between smart contracts and off-chain data sources. In 2025, newer frameworks like Fetch.ai and Bittensor are offering environments where AI models can communicate, train collaboratively, and interact with smart contracts directly.

For example, an AI agent trained on user behavior data can invoke a smart contract that rewards high-value contributors in a decentralized community. The smart contract development company involved must ensure deterministic logic, compatibility with oracle inputs, and privacy protection mechanisms.

Security Challenges with Autonomous AI Systems

As AI agents begin to take on larger roles in Web3 systems, security becomes even more critical. Improperly trained models or exploited AI logic could lead to major vulnerabilities in autonomous smart contract systems.

That’s why AI-auditing tools, formal verification, and simulation testing are becoming core offerings of modern smart contract development services. AI-driven audits themselves are being used to detect bugs, gas inefficiencies, and logic flaws in deployed contracts. Combining human and machine review is key to ensuring safety in fully autonomous systems.

The Human-AI-Smart Contract Feedback Loop

What makes AI agents truly powerful is their ability to adapt based on feedback. In Web3, this creates a loop:

Smart contracts record immutable outcomes of AI actions.

These records are used by the AI agent to improve future decisions.

New decisions are enforced again through smart contracts.

This feedback loop leads to smarter, more efficient, and context-aware decentralized services. It’s also redefining how smart contract development companies build long-term logic systems, placing a stronger emphasis on adaptability and evolution.

Building Autonomous Web3 Projects in 2025

Creating a successful AI-smart contract system requires a collaborative approach. A skilled smart contract development company will work with data scientists, AI researchers, and decentralized architecture teams to ensure interoperability and functionality. Key steps include:

Designing modular smart contracts that can be triggered by AI decisions.

Integrating decentralized oracles and machine learning APIs.

Ensuring security through formal verification and continuous testing.

Enabling governance mechanisms to override AI in case of anomalies.

As these practices become more mainstream, smart contract development services are evolving into end-to-end partners for AI-powered Web3 ecosystems—from ideation and data modeling to deployment and maintenance.

The Future of AI-Smart Contract Systems

Looking ahead, the development of fully autonomous digital economies is on the horizon. Think of decentralized cities where AI agents handle resource allocation, governance, and economic modeling—all powered by a transparent network of smart contracts.

The evolution of AI models—especially multimodal agents capable of language, vision, and planning—is accelerating this shift. In response, blockchain protocols are becoming more composable, privacy-preserving, and AI-compatible.

For businesses, now is the time to explore pilot programs, AI-smart contract integrations, and long-term infrastructure investments. Working with a forward-thinking smart contract development company can provide the strategy and support needed to capitalize on this new frontier.

Conclusion

In 2025, the marriage of AI agents and smart contracts is creating a new paradigm in the Web3 world: systems that think, act, and enforce rules autonomously. This powerful combination is driving innovation across industries, offering scalable and trustworthy automation that reduces costs and improves performance.

Whether you’re building a decentralized finance app, managing logistics, or launching an AI-based DAO, aligning with the right smart contract development services will be essential to unlocking the full potential of autonomous Web3 systems.

[nagentai]

Explore the Leading AI Agent Marketplace for Smarter Automation

Discover the top AI agent marketplace platforms to find, deploy, and monetize intelligent agents. Compare features, use cases, and benefits of joining the AI-driven automation ecosystem.

[familythings]

Microsoft Empowers Customers to Build AI Agents for Routine Tasks

Microsoft is gearing up to let its customers create their own AI agents next month, and it’s a big deal! This is a total game changer from the usual chatbots. These new agents will need very little human help, which means businesses can breeze through their usual tasks a lot faster. What Are Autonomous AI Agents? Unlike basic chatbots that just answer specific questions, these cool autonomous…

[ai-innova7ions]

Video Automatically Generated by Faceless.Video:

Agentic AI refers to AI systems designed to operate as agents that can autonomously perform tasks, make decisions, and interact with their environment and other systems or agents. These AI agents are goal-oriented, capable of sensing their environment, processing information, and taking actions to achieve specific objectives. Unlike traditional AI, which may require explicit instructions for each task, agentic AI systems can act independently within predefined parameters to achieve their goals.

Key Features of Agentic AI:

Autonomy:Agentic AI systems operate independently, making decisions and taking actions without needing constant human supervision.Goal-Oriented Behavior:These AI agents are designed with specific goals or objectives, and they use their capabilities to work towards achieving these goals.Environmental Awareness:Agentic AI can perceive and interpret its environment using sensors, data feeds, or other inputs. It adapts its behavior based on changes in the environment.Decision-Making and Problem-Solving:These AI agents use algorithms to evaluate options, solve problems, and make decisions that align with their goals.Interactivity and Communication:Agentic AI can interact with other systems, agents, or humans, exchanging information and coordinating actions to achieve collective objectives.Learning and Adaptation:Some agentic AI systems can learn from their experiences, improving their performance and adapting to new challenges over time.Task Execution:These AI agents can execute tasks within their domain of expertise, whether it’s navigating a physical environment, processing data, or coordinating with other agents.

Benefits of Agentic AI:

Efficiency in Task Automation:Agentic AI can automate complex tasks, freeing up human resources for more strategic activities.Improved Decision-Making:By processing large amounts of data and considering multiple variables, agentic AI can make more informed decisions than humans might.Scalability:Agentic AI systems can be deployed at scale, managing large, complex operations across multiple domains simultaneously.Adaptability:These systems can adapt to new environments or changing conditions, ensuring that they remain effective even as circumstances evolve.Enhanced Collaboration:Agentic AI can work alongside humans and other AI systems, facilitating better teamwork and coordination, particularly in complex environments.Cost Savings:Automating routine or complex tasks with agentic AI can reduce labor costs and minimize errors, leading to significant cost savings.24/7 Operation:Like autonomous AI, agentic AI can operate continuously, providing services or monitoring systems around the clock.

Target Audience for Agentic AI:

Enterprise Operations:Large businesses use agentic AI to automate complex processes, manage supply chains, optimize logistics, and enhance customer service.Healthcare:Agentic AI is employed in personalized medicine, patient monitoring, and automated diagnostics, where it can operate independently to improve outcomes.Financial Services:Financial institutions leverage agentic AI for automated trading, risk assessment, fraud detection, and customer interaction.Robotics and Automation:In industries like manufacturing, agentic AI powers robots that can operate autonomously in dynamic environments, adapting to new tasks or challenges.Smart Cities and Infrastructure:Governments and urban planners use agentic AI to manage traffic, energy consumption, public safety, and other aspects of urban living.Agriculture:Agentic AI is applied in precision agriculture, where it manages crop monitoring, irrigation, pest control, and other tasks autonomously.Defense and Security:Defense organizations deploy agentic AI for autonomous surveillance, threat detection, and coordination of unmanned systems.Consumer Technology:In the consumer space, agentic AI powers smart assistants, autonomous home devices, and personalized user experiences.

Comparison with Autonomous AI:

Autonomy vs. Agency:While both autonomous and agentic AI operate independently, agentic AI is specifically designed to achieve defined goals within a particular environment, often interacting with other agents or systems to do so.Interaction:Agentic AI often involves more interaction, whether with humans, other AI agents, or systems, as it’s designed to work in a collaborative or multi-agent setting.

Agentic AI is particularly valuable in environments where collaboration, decision-making, and adaptive behavior are essential, offering significant benefits across various industries.

Credit: ChatGPT

[josephkravis]

TOTIC: Envisioning the Future of Operating Systems with LLMs and Autonomous Agents

A futuristic digital interface representing TOTIC, an advanced operating system integrating AI components like LLMs, autonomous agents, and specialized kernels for optimized task management.

Introduction Welcome to an exploration of TOTIC (Timed Orientated Task Initiator Control), a forward-thinking concept designed to revolutionize task management and execution within future operating systems. By integrating Large Language Models (LLMs), specialized kernel functionalities, and autonomous agents, TOTIC aims to create an advanced, efficient, and adaptable system. I hope this post…

[drxxonfly]

sources: lailah gifty akita // unknown // gasoline by halsey (2015) // dead eye dick by kurt vonnegut jr. // unknown // unknown // dxmianwaynesstuff on tumblr // gasoline by halsey (2015) // let me in by cg5 (2024) // let me in by cg5 (2024) // twin fantasy (those boys) by car seat headrest (2011)

prototype autonomous management agent

[funeralprocessor]

I feel like the Primaris should have been the catalyst for like, an imperial civil war. At the very least, much unrest in the house of Guilliman. Their existence, let alone rollout/integration, should have had many chapters absolutely rioting. It should be beyond the pale by several orders of magnitude and be seen as an enormous overreach by the more autonomy loving chapters, a blasphemy by the more orthodox chapters, and an existential threat to chapters with geneseed quirks. Plus anyone with any awareness of the thunder warriors should take one look at them and recognize the writing on the wall. Guilliman should absolutely recognize what they represent, what they imply. Like they're the leading wave of a paradigm shift that doesn't bode well for what came before. And I say this as someone who's not averse to Primaris, I just think they could've, should've, been a waaaay bigger deal. I know they loathe changing the status quo and we're never getting rid of the posterboys but I think we missed out on something interesting.

[mcsm-catified]

Episode seven! Not many new characters, especially compared to last episode. It’s a nice break.

I love her. Pleasantly surprised with how this turned out, I wasn’t sure what to expect. The colours are nice and earthy, and she actually looks old.

And who could forget, the megalomaniacal computer itself. PAMA has to be the most interesting concept for a villain in the game, being able to make Jesse’s friends turn on them against their will. It’s neat.

I’ll also point out that it has a little triangle on its throat, which looks a little out of place, until you look back at Harper’s design.

This is the ✨spooky✨ version for the climax scene, where you battle for the redstone heart. Still haven’t quite figured out how that’ll work in this AU but I’ll think of something. Suggestions welcome.

And as a treat, since there weren’t many new characters, I’ve also included an old WIP animation meme I dug up. It uses my old PAMA design, which is the most blandest thing ever, but idk it’s kinda cool. Might remake it with the new design.

[would-they-be-good-at-asmr]

[jcmarchi]

Iterate.ai Secures $6.4M to Bring Secure, Scalable AI to the Edge of the Enterprise

New Post has been published on https://thedigitalinsider.com/iterate-ai-secures-6-4m-to-bring-secure-scalable-ai-to-the-edge-of-the-enterprise/

Iterate.ai Secures $6.4M to Bring Secure, Scalable AI to the Edge of the Enterprise

In a strategic push to cement its leadership in enterprise-ready artificial intelligence, Iterate.ai has raised $6.4 million in funding. The round is led by Auxier Asset Management and includes prominent investors Peter Cobb, Mike Edwards, and Dave Zentmyer—all former board members of eBags, the $1.65B online travel retailer co-founded by Iterate CEO Jon Nordmark.

This high-profile reunion isn’t coincidental. The founding team behind Iterate has long demonstrated an uncanny ability to anticipate digital trends before they peak. In 2015, they added “.ai” to their name—seven years before ChatGPT pushed AI into the mainstream. That same foresight now powers Generate Enterprise, Iterate’s privacy-first, locally-deployable AI assistant, and Interplay, its patented low-code AI development platform. Together, they’re reshaping how enterprises adopt and scale intelligent software—securely and without vendor lock-in.

A Pragmatic Vision for the Future of Enterprise AI

“Iterate.ai’s approach to AI innovation is not only forward-thinking but also pragmatic,” said investor Peter Cobb, who previously co-founded eBags and served on the board of Designer Brands (DSW). “The team is focused on solving real-world problems—like how to run powerful AI completely offline on an edge device, or how to bring down deployment costs from millions to mere thousands.”

This philosophy is at the heart of Generate: a local-first AI platform designed to run Retrieval-Augmented Generation (RAG) workflows on devices like AI PCs or point-of-sale terminals. Unlike typical cloud-based solutions, Generate performs all language model inference, document search, and automation locally—enhancing both privacy and performance. Its no-internet-needed architecture makes it ideal for sectors like retail, healthcare, and government where data sensitivity and latency are critical.

The Infrastructure for Agentic AI

Iterate’s flagship platform, Interplay, complements Generate by offering a visual, drag-and-drop development environment for building AI workflows, known as agentic systems. These aren’t static chatbots—they’re autonomous agents that can follow logic trees, perform context-aware tasks, and chain together actions across internal documents, APIs, and enterprise databases.

Agentic AI workflows built in Interplay rely on a range of machine learning models—from lightweight Small Language Models (SLMs) optimized for embedded hardware to advanced Large Language Models (LLMs) capable of nuanced language understanding. Interplay also integrates a vector database layer for semantic search and RAG pipelines, ensuring fast and accurate access to unstructured information like contracts or financial filings.

Behind this innovation is Iterate’s co-founder and CTO Brian Sathianathan, a former Apple engineer and one of the original members of its Secret Products Group—the elite team that developed the first iPhone. His experience in hardware-software optimization is evident in how Interplay adapts to diverse chipsets, from Intel CPUs and AMD GPUs to NVIDIA CUDA cores and Qualcomm’s edge processors.

A Legacy of Building and Scaling

Investor and former Staples executive Mike Edwards—who led eBags as CEO after Nordmark—emphasized the trust and track record shared by the founding team. “This is a deeply experienced group that understands product, enterprise go-to-market, and emerging technology. Iterate’s ability to combine a visionary platform with measurable ROI for customers like Ulta Beauty, FUJIFILM, and Circle K is rare in today’s AI landscape.”

Zentmyer, formerly of Lands’ End, praised the team’s diligence in securing critical hardware and distribution partnerships. “Iterate has spent the last 18 months earning trust with giants like NVIDIA, Qualcomm, and TD SYNNEX. Those relationships are hard to win and impossible to fake—they validate Iterate’s enterprise readiness.”

Built for Scale, Designed for Security

Security and data sovereignty are emerging as make-or-break factors in AI adoption. With its air-gapped deployments, role-based access controls, and on-prem inference engine, Iterate gives enterprises complete control over where and how their data is processed. That’s why many customers are choosing Generate and Interplay to run AI across secure government installations, financial institutions, and privacy-conscious retailers.

And unlike traditional AI stacks that require custom fine-tuning or extensive GPU provisioning, Iterate’s platform relies on modular components and zero-trust architecture to deploy rapidly—with or without cloud access.

The Bottom Line

[peterbordes]

Nvidia’s Jensen Huang says AI agents are ‘a multi-trillion-dollar opportunity’ at last nights CES AI keynote. Let that “multi Trillion” sink in, because this global movement is just getting started.

[selfcontainedunivcrse]

also in a timeline in which PB is Cion's little guy things do not fare well for Seren I fear . what if you brought back your dead sibling-pet and they left you for the guy you hate soo bad. And you were deeply unstable enough to try and do botched necromancy instead of like any other way to deal with grief. Someone is dying here

[entrepreneurial1era]

Agentic AI: The Rise of Autonomous Digital Assistants

How Smart Autonomous Agents Are Redefining the Human-AI Relationship

Introduction: A New Era in Artificial Intelligence

Artificial Intelligence (AI) is no longer a distant concept confined to sci-fi novels or the realm of elite researchers. Today, AI is seamlessly woven into our daily lives powering voice assistants like Siri, recommending content on Netflix, detecting fraud in banking systems, and even helping doctors diagnose illnesses faster and more accurately.

But we are now entering a transformative phase in the evolution of AI, one that promises not just efficiency but autonomy, adaptability, and even decision-making capability. At the forefront of this evolution is a new class of systems known as Agentic AI, often referred to as Autonomous Digital Assistants or AI agents.

These next-generation AI systems are not limited to pre-defined scripts or simple automation. Instead, they exhibit goal-oriented behavior, can take independent actions, adapt to feedback, and operate across multiple platforms to complete complex tasks. From managing business operations to coding, designing, researching, and even negotiating, Agentic AI is set to redefine how we work, live, and think.

Why Does This Matter Now?

The rise of Agentic AI is fueled by the rapid advancement of machine learning, natural language processing (NLP), and neural networks. Leading AI models like GPT-4, Claude, and Gemini by Google are already demonstrating capabilities that blur the line between tool and collaborator.

These AI agents aren’t just passive responders they can:

Analyze and interpret vast amounts of real-time data

Make decisions based on defined objectives

Learn from interaction and optimize over time

Perform multi-step tasks autonomously across platforms 

In practical terms, this means we could soon delegate entire workflows from scheduling meetings and writing reports to launching marketing campaigns and conducting customer service to intelligent digital assistants.

A Glimpse Into the Future

Imagine a virtual business partner who not only helps you stay organized but also negotiates contracts, optimizes your website SEO, handles email outreach, and reports performance metrics all without your daily input. This is no longer fiction thanks to innovations in agentic architectures like Auto-GPT, BabyAGI, and tools being developed by OpenAI, this reality is quickly becoming mainstream.

What This Means for You

Whether you're a startup founder, corporate executive, creative freelancer, or student, the rise of Agentic AI signals a massive shift in digital productivity and human-AI collaboration. Understanding how these systems work, their limitations, and their ethical implications will be essential in the coming years.

Stay tuned as we explore how Agentic AI is shaping the future of:

Work and productivity

Entrepreneurship

Customer experience

Education and learning

Human decision-making

Want to stay ahead of the AI curve? Subscribe to Entrepreneurial Era Magazine to get weekly insights on AI-driven innovation, business strategies, and the tools reshaping our world.

What Is Agentic AI?

Agentic AI refers to a new class of artificial intelligence systems that act as autonomous digital agents capable of independently executing tasks, making decisions, and learning from outcomes without constant human oversight. These systems are a significant evolution beyond traditional AI tools like Siri, Alexa, or Google Assistant, which require direct prompts for every action.

Key Concept: Agentic AI possesses "agency" the ability to act on its own in pursuit of a defined goal.

How Agentic AI Works

Unlike rule-based or reactive systems, Agentic AIs:

Plan and prioritize tasks using large language models (LLMs) and advanced reasoning algorithms

Initiate actions proactively based on changing input or context

Monitor and optimize ongoing processes without manual triggers

Adapt to feedback through reinforcement learning or user corrections

Collaborate across systems to accomplish multi-step workflows

This autonomy is what distinguishes Agentic AI from traditional AI. While older systems wait for commands, agentic models can determine “what to do next”, often in real-time.

Real-World Examples of Agentic AI

Here are some powerful tools and frameworks already showcasing the power of Agentic AI:

Auto-GPT: An experimental open-source project that chains GPT-4 calls together to autonomously complete tasks

BabyAGI: A lightweight AI agent that uses a task management loop to accomplish goals

OpenAI’s GPT Agents: Part of OpenAI's Assistant API, these agents can execute code, manage files, and use external tools

Meta’s LLaMA Agents: An open-source effort pushing the boundaries of multi-agent collaboration

From Tools to Teammates

The fundamental shift with agentic systems is that AI is no longer just a tool it becomes a collaborator. These agents can:

Work independently in the background

Schedule and send emails based on intent

Analyze and summarize reports

Interact with APIs and databases

Monitor key metrics and trigger actions based on thresholds 

This shift has vast implications for entrepreneurs, marketers, developers, and enterprise teams, making work faster, smarter, and more human-centric.

Why It Matters

As businesses increasingly adopt automation and AI-driven workflows, the value of Agentic AI lies in:

Scalability: They handle thousands of micro-tasks in parallel

Productivity: Human teams are freed up for creative and strategic work

Cost-efficiency: Tasks traditionally requiring manual labor can be automated

Consistency: No missed follow-ups or human fatigue 

The rise of agentic systems also aligns with major trends in autonomous agents, self-learning AI, and multi-modal interaction the future of digital workspaces.

Learn more about the difference between Generative AI and Agentic AI from Stanford HAI and how it's expected to shape productivity in the next decade.

The Technological Leap Behind Agentic AI

The rise of Agentic AI is not a coincidence, it's the result of rapid advances in multiple fields of artificial intelligence and computing. These systems are driven by a convergence of technologies that allow machines to think, act, and evolve much like human collaborators.

1. Large Language Models (LLMs)

The foundation of agentic AI lies in powerful large language models like OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini. These models can:

Understand complex instructions

Generate human-like text

Analyze unstructured data

Hold multi-turn conversations with contextual awareness 

LLMs give agents the language understanding and generation power to reason and communicate independently.

2. Reinforcement Learning and Agentic Planning

Reinforcement learning techniques like RLHF (Reinforcement Learning from Human Feedback) and goal-based optimization equip agentic systems with the ability to:

Set internal objectives

Learn from trial and error

Optimize decision-making over time 

This makes agents smarter with each interaction, similar to how humans learn through experience.

3. Memory & Long-Term Context

Unlike traditional AI that operates in isolated prompts, agentic systems use memory modules to:

Track goals and user preferences

Recall past conversations and actions

Build on previous outcomes to refine future performance 

For example, tools like LangChain and AutoGPT include memory systems that make agents feel persistent and context-aware, bridging the gap between sessions.

4. APIs and System Integration

Thanks to seamless integration with APIs, webhooks, and automation platforms, Agentic AI can:

Schedule meetings (e.g., via Calendly)

Send emails through Gmail or Outlook

Pull data from CRMs like HubSpot

Update spreadsheets or dashboards

This connectivity turns AI agents into autonomous digital workers embedded across tools and platforms you already use.

5. Multi-Modal Data Understanding

New-generation agents are not limited to text. With multi-modal capabilities, they can process:

Images (object recognition, design feedback)

Audio (voice commands, transcription)

Video (gesture recognition, editing suggestions)

Code (debugging, deployment assistance)

Projects like OpenAI's GPT-4o and Google’s Gemini 1.5 are pushing the boundaries here, enabling agents to perceive and act across sensory input channels.

Continuous Learning & Evolution

Perhaps the most transformative leap is how agentic AIs grow over time. They:

Track long-term goals

Adjust their strategies

Learn from failed outcomes

Reuse patterns that work 

This adaptive behavior, fueled by feedback loops and self-correction, mirrors key traits of human cognition making agentic systems more than tools; they become intelligent teammates.

Use Cases of Agentic AI: Beyond Virtual Assistants

Agentic AI is quickly becoming one of the most transformative tools in both consumer and enterprise landscapes. These AI-powered digital agents go far beyond simple voice commands or chatbot interactions; they're redefining how work gets done across sectors. From automating business operations to revolutionizing healthcare and education, Agentic AI applications are unlocking efficiency, creativity, and personalization at scale.

Business & Marketing: The Next-Gen Workforce

In the business world, agentic AI is functioning as a full-stack digital worker. These intelligent agents can:

Automate CRM tasks by managing leads, sending follow-up emails, and updating pipelines in tools like HubSpot or Salesforce.

Draft personalized marketing content for emails, blogs, or ad campaigns using platforms like Jasper AI or Copy.ai.

Schedule and coordinate meetings across time zones by integrating with calendars and apps like Calendly.

Conduct competitive analysis and summarize market trends in real time, giving businesses a strategic edge.

Software Development: AI That Codes & Maintains

For developers, agentic AI acts as a proactive coding partner. It can:

Debug errors autonomously using tools like GitHub Copilot.

Generate new features based on project specs and user feedback.

Run performance tests, monitor infrastructure health, and auto-scale cloud resources.

Agents can even integrate into CI/CD pipelines to push updates and manage deployment cycles without human intervention.

Education: Personalized, Self-Updating Tutors

In the realm of education, agentic AI is redefining personalized learning. These digital tutors can:

Adapt to a student’s pace and learning style using real-time analytics.

Assign dynamic exercises that reinforce weak areas.

Grade assignments, provide feedback, and curate study materials aligned to the curriculum.

Help teachers reduce administrative load while increasing student engagement.

Explore how Khanmigo by Khan Academy is already pioneering this approach using GPT-based tutoring agents.

Healthcare: Real-Time Patient Support

In healthcare, agentic AI offers solutions that improve both efficiency and patient outcomes:

Triage symptoms and suggest next steps based on input and health records.

Automate follow-up scheduling and prescription reminders.

Monitor vital metrics and send alerts for potential risks in chronic care patients.

Agents can act as digital nurses, assisting medical professionals with real-time insights while improving access for patients especially in underserved areas. Check out how Mayo Clinic is exploring AI-driven care pathways using autonomous agents.

Creative Industries: Empowering Human Imagination

Agentic AI is also reshaping the creative world, helping artists, writers, designers, and marketers create faster and smarter. These tools can:

Draft blog posts, scripts, or story outlines for content creators.

Generate visual ideas or even full designs using tools like Adobe Firefly.

Offer real-time editing suggestions, freeing up time for deeper storytelling or branding work.

Create music, edit videos, or write code snippets for creative tech solutions.

This fusion of human creativity and AI support leads to faster production cycles and higher-quality output.

From Assistance to Collaboration

One of the most profound shifts that agentic AI brings is the transition from tool to teammate. Where older AI models acted like sophisticated calculators or search engines, the new generation behaves more like colleagues who understand context, maintain continuity, and offer proactive input. These agents don’t just wait for tasks, they suggest them. They don’t merely execute, they optimize and innovate.

This changes the human-machine relationship fundamentally. It opens the door to collaborative intelligence, where humans provide vision and judgment, while AI agents handle execution and refinement. The result is a synergistic model where productivity, creativity, and efficiency are amplified.

Challenges and Ethical Considerations

Despite its potential, the rise of agentic AI raises important ethical and operational questions. Trust becomes a central issue. How do we ensure that autonomous systems make decisions aligned with human values? Who is accountable when an AI agent makes a costly mistake? As these agents become more autonomous, there is a pressing need for transparency, auditability, and control mechanisms to prevent unintended consequences.

There’s also the risk of over-dependence. If individuals and organizations begin to rely too heavily on agentic AI, critical thinking and hands-on skills may decline. Furthermore, job displacement in certain roles is inevitable, which necessitates rethinking how education and workforce development can evolve alongside AI.

Privacy is another concern. Autonomous assistants often require access to sensitive data emails, calendars, and financial records to function effectively. Ensuring that this data is used ethically and securely is paramount. Regulation, informed design, and public awareness must evolve in step with these technologies.

The Future: Where Do We Go From Here?

Agentic AI is still in its early stages, but the trajectory is clear. As models become more capable and integration becomes seamless, these digital agents will increasingly handle complex workflows with minimal oversight. The near future could see agents managing entire departments, running online businesses, or supporting elderly individuals with daily tasks and health monitoring.

Imagine logging off work and knowing your AI teammate will monitor your email, respond to routine inquiries, update your CRM, and prepare your reports for the next day all without a single prompt. That’s not science fiction, it's the very real promise of agentic AI.

What this future demands from us is not fear, but responsibility. We must guide the development of these technologies to serve human goals, amplify ethical intelligence, and build a world where AI doesn’t just mimic thought but supports human flourishing.

Conclusion: Empowering the Human Mind Through Agentic AI

The rise of agentic AI signals a fundamental shift in the way we interact with technology. These autonomous digital agents are not here to replace human intelligence, they are here to augment it. By moving beyond simple, reactive tools to proactive and context-aware collaborators, agentic AI extends human capability in areas ranging from decision-making to creativity, productivity, and innovation.

This evolution marks the next chapter of the AI revolution, one where machines are not merely assistants, but intelligent teammates capable of managing complex workflows, learning from feedback, and evolving with us.

As we stand at the edge of this new era, the most important question is no longer “Will agentic AI change our lives?” it’s “How will we choose to harness it?”

With thoughtful design, strong ethical frameworks, and a focus on human-AI collaboration, these technologies can:

Empower entrepreneurs and startups to do more with less.

Revolutionize industries like healthcare, education, and creative media.

Enhance learning, innovation, and accessibility on a global scale.

Want to go deeper? Explore how OpenAI’s AutoGPT and Google’s Project Astra are shaping the next generation of intelligent agents.

Final Call to Action

Are you ready to embrace the future of AI?

Subscribe to Entrepreneurial Era Magazine for more practical insights, case studies, and strategies on integrating Agentic AI into your business, career, or creative journey.

Let’s shape the future together with AI as our co-pilot.

FAQs

What is Agentic AI, and how is it different from regular AI? Agentic AI refers to systems that can operate independently, make decisions, and pursue goals without continuous human guidance. Unlike traditional AI that reacts to commands, Agentic AI takes initiative, plans tasks, and adjusts its behavior based on outcomes. Think of it like a digital assistant that doesn’t just wait for instructions but proactively helps you manage your day, automate work, or optimize decisions. This makes Agentic AI ideal for complex workflows, business automation, and even personal productivity offering a significant upgrade over static or rule-based AI models.

How can Agentic AI benefit my small business? Agentic AI can automate repetitive tasks, manage customer interactions, and even analyze business data to improve operations. For instance, it can handle scheduling, automate emails, manage inventory alerts, and recommend actions based on real-time data. Unlike basic automation tools, Agentic AI acts more like a virtual employee identifying bottlenecks, adjusting priorities, and learning from each decision. This reduces human error, saves time, and allows small business owners to focus on strategy and growth instead of operations. The longer it runs, the smarter and more efficient it becomes.

Can Agentic AI integrate with existing tools like CRMs or project managers? Yes, most Agentic AI platforms are designed to work with existing software like CRMs, task managers, email platforms, and data tools. Integration may involve APIs, plugins, or native connectors that allow the AI to read, analyze, and act on your business data. Once connected, the AI can schedule follow-ups, organize leads, assign tasks, and suggest process improvements without manual input. This seamless integration empowers teams to operate more efficiently, using the tools they already know supercharged by intelligent automation.

Is Agentic AI safe to use with sensitive information? Agentic AI systems are generally built with advanced encryption, access controls, and compliance with data protection regulations (like GDPR or HIPAA, depending on the use case). However, safety depends on the platform you choose. Reputable providers ensure that the AI only accesses necessary data and follows strict protocols for storing and processing sensitive information. Always verify a platform’s security standards, opt for role-based access, and audit activity logs regularly. When implemented correctly, Agentic AI can actually improve security by reducing human error in data handling.

Do I need technical skills to use Agentic AI effectively? No, most modern Agentic AI platforms are designed with user-friendly interfaces, guided onboarding, and natural language instructions. You don’t need to code or understand machine learning. For example, you can ask the assistant to “automate follow-ups for new leads” or “summarize this week’s tasks.” Many systems even learn your preferences over time, making suggestions tailored to your workflow. However, understanding your business processes and goals clearly is important because the AI works best when it knows what outcomes you're aiming to achieve.

How does Agentic AI learn and improve over time? Agentic AI uses machine learning algorithms that analyze data, decisions, and results to improve its performance over time. It tracks patterns, adapts to user preferences, and optimizes processes based on feedback loops. For instance, if you reject certain suggestions, it learns to adjust future recommendations accordingly. Some advanced Agentic AIs also conduct trial-and-error planning, known as reinforcement learning, to fine-tune their strategies. This makes them highly effective in dynamic environments where flexibility, personalization, and long-term optimization are valuable.

Can Agentic AI replace human employees? Agentic AI is designed to augment human workers, not replace them. While it can automate repetitive or data-heavy tasks, humans are still essential for creativity, judgment, and emotional intelligence. For example, the AI might prepare reports, manage appointments, or send follow-ups, but humans will still lead decision-making, handle complex negotiations, and ensure alignment with business values. Think of Agentic AI as a digital teammate, one that handles the busywork so your team can focus on innovation, strategy, and relationship-building.

What industries benefit most from Agentic AI? Virtually every industry can benefit from Agentic AI, but it's especially transformative in areas like customer service, sales, marketing, healthcare, logistics, and finance. For example, in healthcare, an Agentic AI can manage patient follow-ups, insurance verification, and medical reminders. In e-commerce, it can optimize inventory, automate promotions, and analyze customer behavior. Its strength lies in cross-functional utility wherever workflows are repeatable and data-driven, Agentic AI can create massive efficiencies and improve decision quality without ongoing micromanagement.

What should I consider before implementing Agentic AI? Before adopting Agentic AI, define your goals clearly: Do you want to automate tasks, improve decision-making, or scale operations? Evaluate your current workflows to identify areas where autonomy adds the most value. Choose a platform that supports integration with your existing tools, offers robust security, and aligns with your industry needs. Also, prepare your team for collaboration with AI by promoting a culture of experimentation and learning. A thoughtful implementation ensures the AI complements human roles, enhances productivity, and delivers real ROI.

What is the future of Agentic AI? The future of Agentic AI lies in more human-like decision-making, proactive problem solving, and deeper collaboration with both humans and other AIs. We're moving toward AI agents that understand context, maintain long-term goals, and self-optimize with minimal input. In the near future, these assistants will run entire business functions, conduct autonomous research, negotiate contracts, or even design products. They’ll act as intelligent extensions of individuals and organizations blending autonomy with accountability. This evolution marks a shift from using tools to partnering with intelligent agents that think and act independently.