The State of The AI Agents Ecosystem

May 6, 2025

🕒 7 minutes

AI agents aren’t just a futuristic concept anymore. On the contrary, they’re rapidly becoming a foundational part of the modern software stack. But while the promise of autonomous agents is gaining momentum, the real story lies in the messy, fast-evolving ecosystem powering them.

The agentic AI landscape is exploding with new tools, platforms, and frameworks. From foundation models and orchestration runtimes to specialized agent platforms and memory infrastructure, what used to be a narrow niche is now a full-blown tech stack—and it’s shifting fast.

AI Agents are the next big step for LLMs

For tech builders, investors, and enterprise buyers, this ecosystem offers unprecedented opportunities, but also steep challenges. But the main issue could be summarized as follows: we must rethink business systems to work for both humans and AI agents.

We basically need to reshape our digital landscape according to the structure of the current AI agent’s ecosystem. So, in today’s article, we’ll analyze some of the key considerations to build it and the current state of the agentic AI market.

Key Decisions for Building an AI Agent Ecosystem

So, building an AI agent ecosystem is not just about creating smart agents. Businesses must make a series of strategic design decisions that ensure their agents integrate seamlessly into the organization’s operations.

But, to save you some time, in the following sections we summarize some of the most critical decisions that any business must face.

User-in-the-Loop vs. Full Automation

The first key decision when building an AI agents ecosystem is about how much control to give users in the agent’s decision-making process. The question here is if your agents should operate with full autonomy, or if you need user feedback for continuous control and optimization.

This will depend entirely on your industry particularities. For example, since they involve a lot of sensitive data and regulations, healthcare and financial services usually go for automation, allowing users to provide feedback, correct errors, and guide the agent’s learning improves overall trust and effectiveness.

However, it’s important to find the right balance of user involvement to optimize agent performance, enhance training, and ensure accountability in production environments.

Task Planning Design: Human-Defined vs. LLM-Generated

Another key decision is whether to design your AI agent’s ecosystem with human-defined task flows or rely on LLM-driven planning. Some organizations may prefer a more controlled approach where tasks are strictly mapped out, while others might allow the LLM to generate plans dynamically based on input.

In this case, you must watch the peculiarities of workflows that you want to put into your AI agents’ hands. For more complex ones, a multi-agent approach may be necessary, in which case you’ll need to incorporate orchestration tools to manage agent interactions and maintain system stability.

Memory Systems

When building AI agents, there is a diverse combination of options to add context based on your organization’s use case. However, we can reduce this decision into three main choices. These are:

Retrieval Augmented Generation (RAG): Retrieves and integrates knowledge from private and public sources to make LLM responses more accurate. Since LLMs are highly sensitive to input variations, they depend on consistent, well-structured data.

Long-term memory: Larger context windows in LLMs improve their ability to follow multi-step reasoning and maintain context without cutting off information. This also supports more complex tasks and multimodal inputs over longer interactions.

Memory: By incorporating memory features and tools, agents can refine knowledge using external data, recall past actions, and build long-term memory—enabling more adaptive and personalized behavior.

Rigorous Testing vs. Stategic Guardrails

Testing and evaluating AI agent performance requires careful thought. That’s because, unlike traditional software, AI agents behave probabilistically, making traditional testing frameworks less effective.

So, organizations must develop rigorous evaluation loops that incorporate user feedback, step-by-step performance tests, and ongoing monitoring. This process ensures agents adapt and improve over time while meeting the standards for safety, reliability, and real-world performance.

However, as Anthropic CEO has recently admitted, we can’t know for sure how AI systems reason to reach a certain outcome. In short, the same behavior that makes AI agents autonomous is what makes them unpredictable. And it is this lack of understanding what leads to the infamous black box problem.

How the black-box problem affects AI agents?

So, if you must deal with sensitive outcomes, like medical diagnosis in healthcare, for example, the best thing to do is to put guardrails in place to ensure compliance with security standards and ethical guidelines. These include defining execution boundaries, access controls, and auditing features to avoid unintended actions.

The Exploding AI Agents Ecosystem: A Market Snapshot

Just in the past months, the AI agent’s ecosystem has gone from an experimental playground for open-source developers to a full-blown battleground for startups, enterprises, and investors. But, as GenAI capabilities have matured, so too has the demand for tools that go beyond chat and prompt engineering.

We’re heading toward autonomous, multi-step agents that can plan, reason, and act across complex workflows. And we can map this growing landscape into a layered ecosystem that goes from foundational LLMs and memory storage to orchestration runtimes and end-user applications.

In short terms, the basic AI agent architecture for any business can be schematized as follows:

However, what’s clear is this: the AI ecosystem isn’t just growing, it’s fragmenting. And what are driving this growth within the agentic AI ecosystem can be summarized in three key topics:

LLM maturity and openness

With the rise of competitive open models (e.g. Mistral) alongside closed leaders like GPT-4 and Claude, the agent infrastructure has more choices than ever.

VC-fueled innovation

A wave of funding into AI agent startups (especially in horizontal automation and developer tools) has accelerated tooling and experimentation.

Enterprise demand for automation

Businesses are increasingly seeking agent-powered systems that can operate with less human input, creating a strong market pull for usable, scalable agent tech.

The Market Map of the New AI Agents

As we can see, vendors are increasing their offer of agent-powered systems, with more and more products flooding the market. And we can see this in big players: ServiceNow with their new CRM, Salesforce with Agentforce and their new ITSM platform, SAP with Joule Agents, and Google’s AgentSpace Coding assistant are just some of many examples.

However, there’s no dominant quality standard yet. In fact, one of the hottest discussions between users and vendors involves the lack of a clear niche or task in which AI agents excel.

So, instead of one clear winner in this third stage of the AI race, we have a market flooded with emerging frameworks, agent platforms, and new architectural philosophies—each promising to “solve” the problem of scalable, autonomous agents.

To illustrate how this looks like, here you have a market map of the current products within our actual AI agent’s ecosystems:

However, for both buyers and builders, this dynamic presents both an opportunity and a risk. On the one hand, moving too slow can easily leave you behind. On the other, moving too fast could lead your organization to be buried under a complexity of systems or locked into immature stacks.

The Four Layers of the AI Agent’s Ecosystem Stack

To make sense of this chaos, we can try to break the AI agent’s ecosystem down into four functional layers. Each one of them represents a critical building block of agentic workflows and understanding how they interact is key to making smart decisions, whether you’re building a product or selecting a vendor.

So, we can describe the layers of the AI agent stack as follows:

Layer 1: Foundation Models

This is the cognitive core of every AI agent. LLMs like OpenAI’s GPT-4, Anthropic’s Claude, Meta’s Llama, and open-source contenders like Mistral power the reasoning and language capabilities behind agents.

The current tension in the ecosystem lies in choosing between two types of model ownership: proprietary APIs that offer reliability and power, and open-source models that offer customization, data privacy, and cost control.

Layer 2: Agent Frameworks & Runtimes

This is basically the layer of the AI agents ecosystem where the orchestration happens. Frameworks like LangChain, AutoGen, CrewAI, and OpenAgents are racing to become the standard for how agents are created, managed, and made interoperable.

To be short, these are what enable agents to reason across multiple steps, call APIs, use tools, and collaborate with other agents. However, many of them are still in the early-stage—high on ambition, light on stability.

Layer 3: Application Platforms

These are the polished AI agent platforms, often built on top of the earlier layers. They target specific use cases like customer support, sales enablement, software development, or research.

We can think of them as “agent SaaS” solutions. Some aim for horizontal versatility (general agent platforms like Agentforce), while others go deep into verticalized functional systems where agents handle technical and complex tasks with unique data sources (e.g. customer support).

Layer 4: Tooling & Infrastructure

While foundation models and agent frameworks often get the spotlight, this layer is what determines whether your agents can be deployed, scaled, and trusted in production environments. In other words, it’s the foundation of the AI agent’s ecosystem.

But, while critical, right now it’s one of the most underdeveloped areas. However, we can identify some of the main components of this layer. These are:

Memory & Retrieval Systems: Let agents recall past actions using vector databases like Pinecone. Still early and often limited.

Evaluation & Testing: Help measure agent performance, though standards are still emerging.

Observability & Debugging: Provide visibility into agent behavior, decision-making, and errors.

Workflow Orchestration: Coordinate agents across tasks and systems; critical but still maturing.

From Pilots to Production—Scaling AI Agents with Purpose

So, the AI agent ecosystem is maturing, and the next wave of innovation will be driven by intentional design, strong foundations, and business-aligned execution. But scaling agents isn’t just a technical challenge—it’s also a business one.

New pricing models are emerging to reflect how AI agents are actually used in production. From “platform + hire-an-agent” models that treat agents like digital employees, to outcome-based pricing that ties fees to business results, enterprises must navigate this evolving landscape carefully.

And to this we must add the fact that each model requires different levels of buyer risk tolerance and hinges on how much of a job-to-be-done (JTBD) the agent truly owns. Besides, business find many barriers to incorporate AI agents into their workflows.

Harnessing the power of AI agents in business operations

Unclear ROI, complex pricing metrics, unpredictable costs, and compliance risks make many enterprise leaders hesitant. Procurement processes are shifting too—requiring tighter alignment with business units and clearer outcome attribution before budgets are unlocked.

In short, an enterprise’s success with AI agents will depend on more than deploying powerful models. It will require cross-functional buy-in, smart architectural decisions, and clear accountability for results. In this new era, the organizations that win will be the ones that build with purpose—and price with clarity.