Multi-Agent Systems
Two agents working together can do things one cannot. They can also fail in ways one cannot. Here is how to design for the first and contain the second.
A multi-agent system is not a swarm of robots. It is a small team of specialised programs, each with its own context, sharing a goal that none of them sees in full.
The promise is real. Specialisation works. A retrieval agent that knows your knowledge base, a planning agent that decomposes the task, an action agent that calls the right tool with the right parameters: this kind of stack outperforms a single monolithic model on real enterprise work, often by a wide margin. The architecture is now common enough in production that it deserves a proper name and a proper set of operating rules.
The problem is what happens when the agents disagree, or when they agree on something that is wrong. The failure surface compounds. Two agents that are individually 95 percent reliable do not produce a 95 percent reliable system. They produce a system whose reliability depends on how their errors correlate. Sometimes that number is fine. Sometimes it is much worse than either agent alone.
The three patterns that hold up
The teams that ship multi-agent systems successfully share three habits. They write explicit contracts between agents (what each one promises, what it expects), so an upstream change does not silently break a downstream consumer. They centralise shared state into one canonical store rather than letting agents pass conversation history back and forth as ground truth. And they treat the agents as a unit when it comes to oversight, watching the trace end-to-end rather than scoring each agent in isolation.
That last point is where Navedas lives. Single-agent monitors miss the failures that emerge from interaction. The policy layer has to see the chain.
Where it shows up first
Customer support and back-office operations are the early wedges, because both have a natural decomposition: classify, retrieve, decide, act. They are also where mistakes are visible and expensive, which is why most leaders introducing multi-agent flows are also the ones asking what their containment story is. The two questions are inseparable.
Articles & resources
The Customer Experience Time Loop: How Multi-Agent AI Rebuilt It
Disconnected systems used to cause the time loop. Now disconnected agents cause a new one.
Read → ArticleAI Agents: The Future Is Still a Few Years Away. Let's Be Pragmatic.
The honest production gap, and the playbook for shipping anyway.
Read → SolutionAI Risk Containment
End-to-end oversight for multi-agent flows. Catch the chain failures, not just the single-agent ones.
Explore → WhitepaperListening to Your AI: A 2026 Playbook
What to listen for when the conversation isn't with customers anymore. It's with your own agents.
Get the paper →Frequently asked questions
What is a multi-agent system?
A multi-agent system is a setup where two or more AI agents act in the same environment, sometimes cooperatively, sometimes adversarially, often unaware of each other. In enterprise software it usually means a stack of specialised agents (retrieval, planning, action) that hand work between themselves to complete a task no single agent could finish alone.
Why are multi-agent systems harder to govern than single agents?
Because the failure surface is the product of the agents, not the sum. Two agents that are 95 percent reliable individually can produce a system that is far less reliable when their assumptions interact. Memory shared between agents amplifies any error one of them introduces. The audit trail also gets harder: you have to reconstruct who decided what, in which order, with what context.
What patterns make multi-agent systems work in production?
Three patterns we see consistently. Clear contracts between agents about what each one promises and what it requires. A single source of truth for shared state, not a free-for-all on conversation history. And a policy layer that watches the whole flow rather than each agent individually, so it can catch decisions that no single agent would have made on its own.
Are multi-agent systems necessary, or can a single bigger model do the same job?
Sometimes a single model is cheaper and simpler. The case for multi-agent is specialisation: when different parts of the task need different tools, different context, or different oversight rules. The case against is operational: more moving parts, more places to monitor, more chances for the system to surprise you.
Related topics
Govern the chain, not just the agents.
If a multi-agent flow is in your roadmap, your oversight layer needs to see end-to-end. Walk through how a realtime decision layer fits.