Building Production Agent Teams: From Prototype to Scale

The demo worked perfectly. Five agents collaborating. Research, analysis, writing, review, publishing. The output was impressive. The team was excited. They decided to ship it.

Three weeks later they pulled it from production. Not because the agents were not smart enough. Because the system could not handle reality.

This story plays out constantly. And the gap between a prototype agent team and a production system is always the same set of problems. Let me walk through each one and how to solve them.

The Prototype Trap

Prototypes work because they operate in ideal conditions. Inputs are well-formed. Services are responsive. Edge cases do not exist. The demo runs with carefully selected examples that showcase the best behavior.

Production is the opposite of all of that. Inputs are messy. Users misspell things, provide incomplete information, and ask questions the system was never designed for. Services go down. APIs return errors. Network connections drop. And edge cases are not edge cases anymore. They are Tuesday.

The first step toward production readiness is accepting that your prototype is not 80% done. It is 20% done. The remaining 80% is all the boring, critical infrastructure that makes the system work when things go wrong.

Error Handling Is the Entire Job

In a prototype, if one agent fails, you restart the demo. In production, you need defined recovery strategies for every failure mode.

Agent-to-agent communication failures. Agent A produces output that Agent B cannot parse. Maybe the format is wrong. Maybe the content is truncated. Maybe the output is empty. You need validation at every handoff point. If Agent A's output does not meet Agent B's input requirements, you need a retry strategy, a fallback, or a graceful degradation path.

Tool execution failures. The search API returns a 500. The database query times out. The file system is full. Each tool call needs timeout handling, retry logic, and a fallback for when retries are exhausted. The fallback might be "skip this step and note the limitation" or "use cached data from the last successful call."

Model failures. The LLM returns an error. The response is malformed JSON. The response violates format constraints you specified in the prompt. The response is good but took too long. Each of these needs handling. And the handling needs to be different based on the failure type.

Build a failure taxonomy for your agent team. Every interaction point gets a list of possible failures and a defined recovery strategy. This is tedious work. It is also the difference between a system that runs for weeks unattended and one that crashes every few hours.

State Management Is Not Optional

Prototype agent teams manage state implicitly. The output of Agent A goes into the input of Agent B, and that is the state. This works for linear workflows that complete in seconds.

Production agent teams need explicit state management for several reasons.

Resumability. Long-running workflows fail mid-execution. Without checkpointing, you restart from the beginning. With checkpointing, you resume from the last successful step. For a workflow that takes 10 minutes and fails at minute 8, this is the difference between 2 minutes of wasted compute and 10 minutes.

Visibility. When a user asks "what is my agent team doing right now," you need an answer. Explicit state gives you that answer. Implicit state means you genuinely do not know.

Concurrency. When multiple instances of your agent team run simultaneously, they need isolated state. Without explicit state management, concurrent executions can interfere with each other in subtle, hard-to-debug ways.

Implement state as a typed, versioned object that flows through your agent pipeline. Each agent reads from it, modifies it, and passes it forward. Each modification is logged. Each state version is stored. This gives you resumability, visibility, and isolation simultaneously.

Integration Testing Is Non-Negotiable

Unit testing individual agents is necessary but laughably insufficient. An agent that works perfectly in isolation might fail completely when integrated with its team.

Communication integration tests verify that Agent A's output format matches Agent B's expected input format. This sounds trivial. It breaks constantly when you update prompts, because LLMs do not always respect format instructions.

Workflow integration tests verify the end-to-end flow. Start with a real input, run it through the complete agent team, and validate the final output. These tests are slow and expensive because they make real LLM calls. Run them before every deployment anyway.

Load integration tests verify that the system handles concurrent workflows. Start 10 agent team instances simultaneously. Do they interfere with each other? Do they compete for resources? Do they degrade gracefully under load? You will not know until you test.

Chaos integration tests verify resilience. Kill Agent C mid-execution. Does the system recover? Make the database slow for 30 seconds. Does the agent team handle the delay? Return garbage from the LLM. Does the system catch it?

Build a comprehensive integration test suite. Run it automatically. Block deployments that fail it. This is your production safety net.

Quality Assurance for Collective Output

Individual agent quality is necessary but not sufficient. The team's collective output needs its own quality assurance.

A research agent might produce accurate information. A writing agent might produce well-written prose. But if the writing agent does not faithfully incorporate the research agent's findings, the collective output is wrong despite each agent doing its job correctly.

Implement end-to-end evaluation that checks the final output against quality criteria that span the entire pipeline. Is the information accurate? Is the information from the research step actually reflected in the final output? Is the output coherent, or does it read like parts from different agents stitched together?

Track collective quality metrics over time. Individual agent quality can be stable while collective quality degrades because the integration between agents drifts. This drift is insidious because no single agent shows a problem.

The Production Readiness Checklist

Before your agent team goes live, verify every item on this list. Error handling defined for every interaction point. State management with checkpointing implemented. Integration tests covering communication, workflow, load, and chaos scenarios. End-to-end quality evaluation running. Monitoring and alerting configured. Rollback procedure documented and tested. Cost limits set per execution. Graceful degradation paths defined.

This checklist is not comprehensive. It is the minimum. Every item on it exists because a team shipped without it and regretted it. Learn from their incidents instead of creating your own.