Building reliable agent workflows
Design patterns for safer AI automation in production.
Agent workflows fail in predictable ways when state is ambiguous, tools are too broad, or recovery paths are undefined. Production systems need the same engineering discipline as any critical service.
These are the patterns we use internally and recommend to teams shipping customer-facing automation.
Narrow tools, explicit outcomes
Agents behave better when each tool has a tight contract: inputs, outputs, and failure modes are documented and enforced.
We avoid mega-tools that try to do everything, because they hide decisions that should be visible in logs and tests.
Human checkpoints that scale
Not every step needs a human, but the steps that do should be obvious and fast. That means good defaults, clear diffs, and batch review where it helps.
The goal is to keep humans in the loop without turning them into a bottleneck for routine approvals.
Evaluation as a product habit
We keep golden sets for high-risk flows and track regressions when prompts, tools, or retrieval settings change.
If you cannot explain why an agent output changed week to week, you do not yet have a production workflow. You have a demo.