AI Agents, Built Like Microservices.
Composable micro-agents with clear contracts, predictable behavior, and production-grade reliability.
Monolithic agents are brittle.
One giant assistant blends intent, execution, policy, and formatting into a single prompt. That means opaque failures, unpredictable drift, and testing that feels like archaeology.
Hard to test
Behavior is tangled, so you cannot isolate a failure or add reliable fixtures.
Hard to govern
Permissions and tool usage bleed across every task with no boundaries.
Hard to scale
Each improvement risks regressions everywhere else.
Micro-agents with crisp contracts.
Stop building monolithic AI assistants. Build an agent mesh of small agents with crisp contracts. Each agent has a single responsibility, typed I/O, and explicit guardrails.
Composable workflows
Micro-agents chain together like microservices and can be orchestrated in parallel.
Replaceable components
Swap one agent without retraining the whole system or rewriting your prompts.
Observable behavior
Metrics by agent: latency, cost, success rate, and failure modes.
How it works
Request → Router → Agents → Tools → Result. Each hop is a typed contract with observability and policy checks.
Doctrine: micro-agents as services
Single responsibility
Less prompt bloat, less ambiguity.
Contracts
Inputs + outputs are typed and enforced.
Replaceability
Swap one agent without retraining the whole system.
Observability
Latency, cost, and success rate per agent.
Governance
Permissions per agent; finance cannot email.
Versioning
Agents are APIs; release v2 safely.
Fine-grain vs coarse-grain
Fine-grain micro-agents are strict, typed, and predictable. Coarse-grain agents plan and adapt. The best systems blend both.
| Aspect | Fine-grain micro-agents | Coarse-grain agents |
|---|---|---|
| Scope | Single task, strict schema | Multi-step, flexible scope |
| Best for | Extraction, validation, compliance | Planning, ambiguous requests |
| Tradeoff | More orchestration complexity | Harder to test and govern |
Reference architecture
Request → Router → Agents → Tools → Result. The mesh stays small, typed, and observable.
Example agent catalog
Condenses long content into concise briefings.
Produces structured output with strict schema.
Routes intents with deterministic labels.
Generates constrained tone and format.
Tool-gated research with safety checks.
Redacts sensitive data and enforces compliance.
FAQ
What is a micro-agent?
It is a small, single-purpose agent with explicit inputs, outputs, and guardrails.
How do micro-agents improve reliability?
Failures are isolated, retryable, and easy to observe.
When should I use coarse-grain agents?
Use them for planning and ambiguous tasks, then hand off to fine-grain agents for execution.
How do contracts work?
Each agent has typed input/output schemas, tool permissions, and eval rubrics.
Can I run micro-agents in parallel?
Yes. Fan-out and join patterns are built-in to the mesh design.
Is this approach production-ready?
Yes, because it mirrors proven microservice discipline with observability and governance.