It seems like just yesterday that Artificial Intelligence (AI) was something reserved for the distant future. Then suddenly, whether it arrived all at once or gradually crept in, AI was everywhere. Practically overnight, we found ourselves surrounded by new tools, new possibilities, and new risks. These shifts have forced us to rethink how technology fits into our lives, our work, and the decisions we make each day.
Among the most transformative developments is the rise of AI agents. From customer service bots that manage entire conversations to autonomous assistants that coordinate tasks across apps, we are now entering a phase where AI agents operate independently. They analyze, decide, and in many cases, take action on our behalf.
This rapid evolution brings with it a critical question: Should there be a standard for AI agents? As adoption grows across industries and platforms, the conversation around AI agent standards is no longer speculative. It is increasingly urgent.
What Are AI Agents?
Unlike traditional AI models that perform isolated tasks, AI agents are systems designed to perceive their environment, make decisions, and act accordingly. Sometimes they work alone. Other times, they interact and collaborate with other agents in complex, coordinated systems.
They are more than smart tools. AI agents are digital entities embedded in workflows, often operating across platforms, applications, and even organizational boundaries.
You’re likely already encountering AI agents in a variety of contexts. Personal productivity agents, such as Microsoft Copilot or ChatGPT plugins, assist with everything from drafting content to managing schedules. In customer service, support agents now handle entire interactions from start to finish without human intervention. In technical environments, automation agents are taking on tasks in DevOps and cybersecurity, improving efficiency and reducing the need for constant oversight. Meanwhile, multi-agent systems are being used to coordinate complex workflows in areas like logistics, scientific research, and large-scale simulations.
These systems go far beyond simple automation. They adapt, respond, and take initiative, which introduces a new set of concerns around trust, transparency, and interoperability.
The global AI agent market is projected to grow from $5.4 billion in 2022 to $47.1 billion by 2030, at a CAGR of 44.8%, according to market forecasts. With such explosive growth, the need for clear AI agent standards becomes increasingly critical to ensure secure and consistent development across platforms.
Why the Conversation Around Standards Is Starting
Right now, the world of AI agents is something of a free-for-all. Developers, companies, and open-source communities are each building agents using different architectures, communication methods, security approaches, and ethical guidelines.
You could go right now and search online for “how to create an AI agent,” follow a tutorial, and deploy your own without ever needing to consider how it might interact with others—or how it could be misused.
That is exactly the problem. Without established AI agent standards, we face serious challenges when it comes to coordination, accountability, and safety.
By 2025, 85% of enterprises and 78% of small and medium-sized businesses plan to integrate AI agents into their operations. As adoption accelerates, establishing AI agent standards will be essential to manage risk, promote interoperability, and protect user data.
Interoperability, Security and Trust
AI agents need to work together across organizations and systems, which means interoperability is key. Without standard protocols and a shared language, agents become siloed and inefficient. Trust is another major concern. If an AI agent is making decisions or executing actions inside a system, how do you trust that it will behave as expected? Standards can help define the minimum expectations for identity verification, access control, and transparency in decision-making.
Accountability, Compliance, and Ethical Guardrails
We also need to think about accountability. What happens if an agent makes a mistake or gets manipulated into violating a policy? Without consistent rules around auditing, logging, and compliance, mistakes can go unnoticed or unresolved. Finally, there are ethical questions. From protecting user privacy to reducing bias, standards offer a structured way to build ethics directly into how AI agents behave.
Ninety-five percent of businesses cite data privacy and security as top concerns in agentic AI development. Organizations with strong AI security protocols are 40% less likely to suffer major data breaches, further reinforcing why AI agent standards must address access controls, data governance, and trust frameworks.
What Might an AI Agent Standard Look Like?
For AI agents to safely operate in shared environments, we will need a common set of rules and structures. These may include a universal communication protocol that allows agents to “talk” across systems, along with defined identity and trust frameworks that verify who or what an agent is.
Standards would also need to include clear boundaries around what an agent is allowed to do and under what circumstances. Transparency requirements would help ensure that human users can understand the logic behind an agent’s actions. A registry or certification model could help users and companies identify which agents meet minimum safety and ethical criteria.
This situation is similar to the early days of the internet. Before HTTPS, HTML, and standard APIs, every platform was isolated. Only once shared standards emerged did the internet become scalable, interoperable, and secure. AI agents may need a similar foundation.
Is There a Precedent?
Yes — and no.
There are some encouraging examples we can look to. OAuth helped standardize secure authorization across platforms. OpenAI has introduced function calling and plugin interfaces to give agents more structured interactions. AutoGPT brought forward innovations in how agents manage task memory and iterative reasoning. Organizations like MLCommons and the IEEE have been actively working on frameworks to support ethical AI development.
However, none of these efforts fully address the specific and growing complexity of multi-agent systems. What’s missing is a comprehensive, scalable standard that can cover identity, behavior, communication, and compliance across a wide range of environments and use cases. The existing pieces are promising, but they don’t yet form a unified foundation.
Why the Urgency?
The need for AI agent standards is growing rapidly. Large Language Models (LLMs) are evolving from tools into full-fledged platforms that support entire ecosystems of agents. At the same time, businesses are no longer experimenting—they’re actively deploying AI agents in production environments where performance, trust, and accountability matter.
Open-source multi-agent frameworks like CrewAI, AutoGen, and LangGraph are also gaining momentum, making it easier than ever for developers to build and deploy agents with complex capabilities. What’s more, we’re beginning to see agents interacting with each other without direct human input, forming independent systems that operate in increasingly dynamic ways.
With each new use case comes a new set of risks. Fragmentation, inconsistent behavior, security vulnerabilities, biased decision-making, and loss of control are all very real possibilities in the absence of clear AI agent standards. The faster adoption accelerates, the more urgent it becomes to establish a shared foundation that prioritizes safety, transparency, and interoperability.
Who Should Set the Standards?
If we accept that standards for AI agents are necessary, then the next big question is: Who decides what they look like?
Government and Regulatory Bodies
Agencies like NIST in the United States—or their global counterparts—could establish baseline rules around safety, transparency, and access. Government regulation plays a critical role in setting guardrails that protect the public interest. However, regulatory processes often move more slowly than the pace of AI innovation, making it difficult to keep up with fast-evolving technologies.
Industry Consortia
Industry groups may offer a more flexible and collaborative path forward. Much like the W3C shaped the early internet, alliances of tech companies, researchers, and standards organizations could work together to develop technical guidelines that are both practical and forward-looking. These collaborations can evolve alongside the technology itself.
Open-Source Communities
The developers building today’s AI frameworks—like those behind LangGraph, CrewAI, and AutoGen—are already solving real-world interoperability challenges. Their hands-on experience gives them unique insight into what’s needed and what’s feasible. Including their voices ensures that standards reflect on-the-ground realities rather than top-down assumptions.
Private Companies
Tech giants such as Microsoft, OpenAI, Google, and others are already shaping the agent ecosystem through their platforms and tools. While their innovations are leading the charge, relying solely on private companies to define the rules brings significant risks. Commercial incentives may not always align with broader ethical or societal needs.
A Hybrid Model
The most promising path may be a hybrid approach. Government agencies can establish broad frameworks for safety and accountability. Industry consortia can define technical best practices. Open-source communities can drive transparency and adaptability. Together, these groups can create standards that are inclusive, practical, and responsive to change.
Ultimately, this isn’t about giving one group the final say. It’s about making sure that AI agent standards are developed through a shared, multi-stakeholder process that balances innovation with responsibility.
AI agents are credited with boosting operational efficiency by up to 30% in sectors like manufacturing and logistics. They can also increase business revenue by 6 to 10%. These benefits come with higher stakes, making the presence of strong AI agent standards all the more important to support sustainable growth.
Standards Will Shape the Future of AI Agents
AI agents are already changing how we interact with technology. And their capabilities are only going to grow. Without clear AI agent standards, we could end up with a future defined by fragmentation, security flaws, and untrustworthy systems.
Just as important as the standards themselves is how they are created. If we wait too long or leave the decisions to a handful of companies, we risk locking in systems that are misaligned with broader human values like fairness, security, and accessibility.
The conversations we start now will determine the kind of digital world we live in tomorrow. So the next time you interact with an AI agent, ask yourself: Who does it serve? How does it behave? And has it earned your trust?
This is not a question for the future. It is a question for right now.
Personal Note from the Author:
Artificial Intelligence—and all the aspects surrounding it—fascinates me tremendously. As the field evolves, I find it exciting to watch not just the technology itself, but also the ripple effects it has on how we work, think, and interact with the world. I wrote this article because I saw the conversation around standards taking shape almost immediately after ChatGPT became publicly available in November 2022. I vividly remember participating in an open forum just weeks later, where people were already asking tough questions: How will we know where the information is coming from? Who ensures it’s accurate and not harmful? Who decides what’s appropriate? That early exchange has stuck with me and continues to shape how I think about the need for AI agent standards. AI continues to evolve, and I hope you’re along for the ride.