You've probably seen the demos. An AI model kicks off a task, hands pieces of it to other AI models, and somehow the whole thing gets done. Emails drafted, code reviewed, reports summarized — all without a human in the loop. While a single agent doing one thing is impressive, the true paradigm shift occurs when transitioning from single-agent to multi-agent AI systems.
It looks like magic. It isn't.
There's one idea underneath all of it, and once you see it, how AI agents communicate makes a lot more sense.
An AI agent isn't magic. It's a reasoning engine that makes decisions based on what it knows at a given moment. That "what it knows" is its context — the information available to it right now.
Context is everything. Give an agent the wrong context, or incomplete context, and it makes bad decisions. In fact, if an LLM agent *is* making wrong decisions, it's almost always due to missing data rather than a failure of the model's reasoning capabilities. Give it the right context, and it does genuinely useful work.
So when agents talk to each other, what are they actually exchanging? Context. Every interaction between agents is one of two things:
That's it. Two operations. Every multi-agent workflow, no matter how complex it looks, is built from retrieval and mutation.
Understanding retrieving context vs mutating context in agents is the foundational key to designing systems that actually work.
Once you accept that context is the currency of agentic systems, the next question is obvious: where does it come from? Three places:
To move beyond isolated tasks, you have to connect these operations. Chaining AI agents for business workflows means designing a sequence where one agent's mutated state automatically becomes the retrieved context for the next agent in line. The information multi-agent LLM systems share during this handoff — such as JSON payloads, updated database rows, or summarized text — must be standardized. If the schema breaks between agents, the context is lost, and the workflow fails.
Most organizations are at the "single agent does one thing" stage right now. That's useful. But the interesting shift happens when you start chaining agents together — when one agent's output becomes another agent's input, and systems start making decisions that humans used to make.
When that happens, the bottlenecks usually aren't the AI models themselves. They're the plumbing: how agents access context, how reliably they can mutate state across systems, how you observe what's happening, how you keep it secure.
The teams shipping the most capable agentic workflows aren't necessarily using the smartest models. They're the ones who figured out the context problem.
*Kong builds the connectivity layer that AI agents rely on — the infrastructure that lets agents retrieve and mutate context across any system, reliably and at scale. *[_*Learn more about Kong AI Gateway →*_](https://konghq.com/products/kong-ai-gateway)_*Learn more about Kong AI Gateway →*_
**What is AI agent context management?**
AI agent context management is the process of controlling the information an AI model has access to at any given moment. Because agents are reasoning engines, they rely entirely on their current context to make decisions. Managing this context involves ensuring agents can reliably retrieve accurate data and mutate (update) state across systems without losing information.
**Why is context important for AI agents?**
Context is important because it acts as the agent's temporary reality. Without the right context, an AI agent cannot make accurate decisions, leading to hallucinations or incorrect actions. Providing precise, up-to-date context ensures the agent's outputs are grounded in reality, making multi-agent systems reliable for business workflows.
**What is the difference between retrieving and mutating context?**
Retrieving context is a read operation—it happens when an AI agent fetches information it doesn't currently possess from a database, API, or another agent (e.g., checking inventory). Mutating context is a write operation—it happens when an agent changes the state of the world (e.g., updating a CRM record or sending an email), which then creates new context for subsequent agents.
**How can I prevent my LLM agent from making wrong decisions?**
If an LLM agent is making wrong decisions due to missing data, the solution is to improve its context retrieval plumbing. Ensure the agent has direct API access to external tools (like your CRM or internal wiki) and maintains an internal memory of past interactions so it bases its reasoning on complete, factual data rather than assumptions.
**How do you share state across autonomous agents securely?**
Sharing state securely requires robust plumbing between agents. Instead of passing raw, sensitive data directly through LLM prompts, secure multi-agent pipelines use standardized schemas (like structured JSON) and rely on secure databases to hold the mutated state. Agents are granted scoped API permissions, ensuring they can only retrieve or mutate the specific context necessary for their designated task.
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