
AI Agent Loops and Graphs: A Production Field Guide
Turn agent loops, graph orchestration, scoped retries, evidence gates, and human approval into one testable production architecture.
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Turn agent loops, graph orchestration, scoped retries, evidence gates, and human approval into one testable production architecture.

A practical evidence contract for producing faithful, useful AI decision explanations—and rejecting plausible rationales that cannot be proved.

A source-checked build guide for six ledger-owning agents, one routing desk, evidence-bound handoffs, and human control over consequential actions.

A control architecture for separating proposal, approval, execution, and reconciliation when AI can trigger consequential business actions.

A practical change-control architecture for deciding what must be versioned, tested, canaried, and restored together when an AI system evolves.

A practical architecture for deciding when AI-generated content may leave, must be minimized, needs human review, or must be blocked.

A field guide to rebuilding, evaluating, cutting over, and rolling back vector search when the embedding model, chunks, or index must change.

A field guide to units, currency, precision, rounding, and deterministic calculation before AI-generated numbers can create business effects.

A field guide to closing intake, authority, dependencies, work, evidence, and data so an AI service can be replaced or retired with a verifiable end state.

A practical guide to deciding when AI should answer, seek evidence, defer, or refuse using calibrated signals, risk–coverage curves, and fallback capacity.

A field guide to admitting, quarantining, or rejecting MCP servers, plugins, and agent tools using provenance, capability tests, and enforceable runtime limits.

A field guide to baselines, delayed outcomes, change attribution, and deciding when deployed AI should be watched, constrained, rolled back, or rebuilt.