We solved intelligence. Now we need coordination.
That's the uncomfortable truth nobody puts in the launch tweet. Everyone's racing to ship agents — tool-calling, autonomous workflows, agents that spin up other agents. It feels like progress. And it is, right up until you have three agents that don't agree on what just happened.
Everyone Is Building Agents
Agents are everywhere right now. Every framework, every cloud provider, every weekend project has one. Give a model some tools, a loop, and a goal, and call it autonomous.
For a single agent, this works shockingly well. The hype is earned — up to a point.
Single-Agent Systems Are Easy
One agent is a manageable problem. Context lives in one place. State lives in one place. When something breaks, you read the trace top to bottom and you find it.
Debugging a single agent is basically debugging a function call with extra steps. Annoying sometimes, but tractable.
Then you add a second agent.
Complexity Explodes With Multiple Agents
This is where most teams get the surprise.
Two agents means delegation loops — who's actually responsible for finishing the task when both agents think the other one has it. It means ownership confusion — which agent's output is authoritative when they disagree. It means state transfer that's lossy by default, because passing context between agents isn't a function call, it's a translation problem.
Add a third agent and you get conflicting outputs — two agents independently producing valid-looking answers that contradict each other, with no built-in mechanism to reconcile them. You get memory synchronization issues, where one agent's understanding of "current state" drifts from another's. And you get failure propagation — one agent silently failing or hallucinating, and every downstream agent inheriting that bad context as ground truth.
None of this is a model problem. The models are fine. This is a systems problem. And systems problems don't get solved by making the model smarter.
The Missing Layer: Orchestration
What's missing isn't intelligence. It's the layer that coordinates intelligence.
An orchestration layer needs to handle routing — deciding which agent gets which piece of work and why. It needs governance — rules about what an agent is and isn't allowed to do, and what happens when it oversteps. It needs a coordination mechanism that isn't just "agents talking to each other and hoping for the best."
This starts to look like APM for agents — observability into who did what, when, and why, across a chain of autonomous decisions instead of a single request. Cross-agent collaboration needs a contract, not a vibe. Workflow supervision needs to exist above the agents, not be improvised by one of them.
Right now, most teams are building this layer by hand, badly, because nobody told them they'd need it.
Agentic Systems Are Becoming Distributed Systems
Here's the part that should change how you think about this.
Multiple agents talking to each other, each with their own state, each capable of failing independently — that's not a new problem. That's a distributed system. We've seen this movie before.
Agents are starting to look a lot like microservices: independently deployed, independently fallible, each with a narrow responsibility. And orchestration is starting to look a lot like a service mesh or a workflow engine — the layer that handles retries, routing, timeouts, and failure isolation so individual services don't have to reinvent it.
The teams that win at multi-agent systems won't be the ones with the cleverest individual agent. They'll be the ones who treat agent coordination with the same rigor we already apply to distributed systems — because that's what it is.
The Future Is Coordinated Intelligence
The next leap in AI systems isn't a smarter model. It's a system that knows how to coordinate the models it already has.
We spent years solving for intelligence. The next few years are about solving for coordination.
— Cheers, NP