AI agents are becoming easier to connect through APIs, MCP, A2A and other protocols. But interoperability alone does not create reliable enterprise AI. The harder challenge is making agents, systems and people work together reliably across real business workflows.
Building an AI agent has become considerably easier. Models can reason across complex instructions, frameworks can manage tools and state, and developers can assemble useful agents in days rather than months.
The harder problem begins when those agents have to work with other agents, enterprise applications, data sources and people.
This is where the conversation around agentic AI is starting to shift. The question is no longer only whether an agent can perform a task. Increasingly, it is whether multiple AI capabilities can work together reliably enough to participate in an actual business process.
That distinction matters.
An agent that summarizes a document, analyses a dataset or retrieves information from an enterprise system may work very well in isolation. But an enterprise workflow rarely consists of one isolated activity. It has dependencies, permissions, approvals, exceptions, handovers and consequences for what happens next.
Connecting these pieces is becoming easier. Making the resulting system dependable is a much larger engineering problem.
Interoperability Is Becoming an Important Part of the Agentic AI Stack
The emerging agent ecosystem is being built across different models, frameworks, programming languages and infrastructure environments. Naturally, there is growing interest in common ways for these components to communicate.
Traditional interfaces such as REST APIs and gRPC continue to provide established ways for applications and services to invoke capabilities. gRPC, for example, is designed around remotely callable services with defined methods and message structures, making it widely applicable to distributed systems.
Newer standards are addressing problems that are more specific to AI systems.
The Model Context Protocol, or MCP, provides a standard way for AI applications to connect to tools, resources and other external capabilities. Its current ecosystem allows servers to expose elements such as tools, resources and prompts to compatible AI applications.
Agent2Agent, or A2A, tackles a different problem. It is designed for communication between independent agents, including discovery of capabilities, task management and collaboration across agents that may have been created with different frameworks or by different vendors. The protocol has since moved into the open-source ecosystem and, as of 2026, is part of the Agentic AI Foundation.
JSON-RPC, webhooks and event-driven messaging add other interaction patterns for structured calls and asynchronous work.
These technologies should not necessarily be viewed as competing solutions. They operate at different parts of the stack.
An enterprise system might use an API to expose a service, MCP to make a capability available to an AI application, A2A for collaboration between independent agents, and events or webhooks to signal that something has changed elsewhere in the workflow.
Interoperability is therefore becoming less about finding one universal protocol and more about allowing different components to interact through clearly defined interfaces.
But that only gets us to connectivity.
An Agent Can Succeed While the Workflow Still Fails
Consider a simple multi-step enterprise process.
One agent retrieves information from several documents. Another analyses it. A third prepares a recommendation. A fourth updates an enterprise application or triggers the next stage of work.
Every agent could technically complete its assigned task.
The workflow could still fail.
Perhaps the first agent retrieved an older version of a document. Perhaps two systems represented the same customer or asset differently. Perhaps the analytical agent produced a recommendation without knowing that an approval was still pending. Or perhaps an upstream system changed after one agent completed its work but before another acted on the result.
None of these situations necessarily means that the underlying model is poor.
The problem lies in the system around the models.
Once agents participate in multi-step processes, execution state becomes important. The system needs to know what has happened, what is currently happening, what still needs to happen and what should occur when something does not happen as expected.
The same applies to context.
In a conversational AI system, context often refers to information given to a model. In an enterprise workflow, operational context is much broader.
It can include which customer, machine, order or case is being handled; which version of a document was used; who initiated the request; what permissions apply; which previous actions have already taken place; whether an approval exists; and what exception conditions have been triggered.
Passing text from one agent to another does not automatically preserve that context.
This is where an interconnected collection of intelligent agents starts to look less like a chatbot architecture and more like a distributed operational system.
Reliability Becomes an End-to-End Question
Distributed systems have dealt with partial failures for decades.
A service can become unavailable. A request can time out. The same message can arrive twice. One part of a transaction can succeed while another fails.
Agentic systems inherit many of these familiar engineering problems, but they introduce additional uncertainty because some components are probabilistic.
An agent may return an answer that is syntactically valid but semantically incomplete. A tool call may execute correctly even though the agent selected the wrong tool. Another agent may receive the result and continue processing without recognizing that something upstream was uncertain.
That changes the meaning of reliability.
It is no longer enough to ask whether an API returned HTTP 200, whether a model produced a response or whether an individual agent marked its task as completed.
We have to ask whether the intended business outcome was achieved.
As workflows become more complex, questions around retries, timeouts, checkpoints, idempotency, state persistence, dependency management and recovery become increasingly relevant.
Some failures can be retried automatically. Others should stop the workflow. Some require a fallback model or another agent. Others require a person to review what happened before anything continues.
The difficult part is deciding which is which.
Human Intervention Is Not a Failure of Agentic AI
Much of the language around AI agents focuses on autonomy.
In enterprise environments, autonomy is better understood as something that exists within boundaries.
There are processes where an AI system can reasonably complete a task from beginning to end. There are others where it should prepare information and leave the final decision to a person. And there are situations where the system should normally act autonomously but escalate unusual cases.
These are very different operating models.
A procurement agent might be allowed to collect supplier information and prepare a comparison, while a person approves the commercial decision. A maintenance workflow might automatically analyse historical records and suggest likely causes but require an engineer before work is authorised. An insurance workflow might gather documents and prepare a case while specific financial or regulatory decisions remain with authorised personnel.
The important design question is therefore not simply, “Can the agent do this?”
It is also, “Under what conditions should it be allowed to do this?”
That brings authorization, provenance, traceability and auditability into the core architecture rather than treating them as controls added after an AI prototype has already been built.
It also changes the role of people.
Human intervention can be designed around approvals, exception handling, escalation or decision authority. A well-designed agentic workflow should make those boundaries explicit rather than leaving a person to discover them after something goes wrong.
Why This Starts Looking Familiar to Operations Teams
There is an interesting parallel with industrial and enterprise operations.
A manufacturing process may consist of several individually efficient activities. But the overall process can still perform poorly because material waits between stages, information arrives late, responsibilities are unclear or exceptions are not handled quickly enough.
Optimising one machine does not automatically optimise a production line.
The same principle applies to information workflows.
A highly capable model does not automatically create a highly capable business process. Neither does adding more agents.
What matters is how information moves, how decisions are handed over, where dependencies exist and what happens when actual operating conditions differ from the expected path.
This is why agentic AI increasingly becomes a systems-engineering problem.
Models remain important. Retrieval quality remains important. Agent reasoning remains important.
But once AI becomes part of an operating process, the interfaces between these capabilities become just as important as the capabilities themselves.
Enterprise Context Makes the Problem Harder
Enterprise environments introduce another complication: most organisations are not starting from a clean technology stack.
Important information may already live across ERP platforms, CRM systems, databases, document repositories, engineering applications, spreadsheets and specialised operational systems.
Those systems were often designed at different times for different purposes.
The same business entity may appear under different identifiers. Documents may have multiple revisions. Access rights may differ across systems. Some information may be highly structured, while important context may exist only inside a PDF, email or human workflow.
Introducing agents on top of this environment does not make these inconsistencies disappear.
In fact, AI can expose them more quickly.
A model can retrieve information extremely fast, but speed is not particularly useful if the underlying record represents the wrong asset, an obsolete specification or information the user should not have been allowed to access.
For enterprises, this means the path towards agentic AI may sometimes begin before the agent itself.
Data readiness, identity and entity resolution, access controls, provenance and enterprise knowledge can become prerequisites for reliable execution.
That does not mean every organisation needs to rebuild its entire data architecture before experimenting with AI.
A more practical approach is often to begin with a clearly bounded workflow, understand the systems and information required for that workflow, establish the necessary controls and then expand from there.
The Question Enterprises Should Ask Is Changing
Early enterprise AI discussions frequently started with the model.
Which model should we use? Should it run in the cloud? Should we use retrieval? Which agent framework should we adopt?
Those remain valid technical questions, but they are increasingly downstream of a more important one:
What business process are we trying to improve, and what would a successful outcome actually look like?
Once that is clear, the technical architecture becomes easier to reason about.
A workflow may need one agent rather than ten. Another may not need an agent at all. Some processes require real-time execution, while others can operate asynchronously. Certain activities may justify autonomous actions; others may only require better decision support.
This is also why enterprise AI initiatives benefit from measurable operational outcomes.
The useful metrics are not limited to model accuracy.
Depending on the process, success could mean reducing turnaround time, lowering manual effort, improving consistency, reducing missed exceptions, shortening decision cycles or giving employees faster access to reliable information.
The AI architecture should ultimately serve those outcomes rather than becoming the outcome itself.
From Agent Interoperability to Operational AI
The growing adoption of common interfaces and protocols is an important step for agentic AI.
It means developers do not necessarily need to create bespoke integrations for every combination of tools, applications and agents. Open standards can make capabilities more portable and ecosystems more composable.
But interoperability should not be confused with operational readiness.
Enterprises ultimately need more than agents that can find and call each other.
They need systems that understand context, respect permissions, maintain state, recover from failure, preserve traceability and know when a human decision is required.
Most importantly, they need those systems to improve an actual business process.
At Poniak Labs, these questions are influencing how we think about agent development, interoperability, orchestration and distribution as we continue building our platform and working through enterprise use cases.
Our view is that the interesting opportunity is not simply to put more agents inside organisations. It is to understand where specialised AI capabilities can meaningfully participate in existing operations and then engineer the surrounding system so that those capabilities can be used reliably.
For enterprises considering agentic AI, that may also be a useful place to begin.
Start with a real process. Understand its information, dependencies, handovers, controls and exceptions. Identify where AI can genuinely improve the flow of work.
Then decide how much intelligence – and how much autonomy – the process actually needs.
Because getting agents to communicate may soon become the easy part.
Getting the entire system to work reliably is where much of the real engineering is only beginning.