How to Manage Dependencies Between AI Agents

Executive Summary

Direct answer: Manage agent dependencies with explicit service contracts (inputs, outputs, errors), versioned skills in a central registry, and orchestration as DAGs using queues, timeouts, retries, and idempotency keys. Enforce SLAs and circuit breakers, capture end-to-end traces and costs, gate sensitive actions with policy validators, and ship changes via feature flags, canaries, and rollbacks.

Dependencies appear wherever one agent calls another agent or shared service—LLMs, enrichment, routing, calendaring, file storage. Treat each dependency as a product with a contract: schemas, required/optional fields, auth, rate limits, expected errors, and reason codes. Register agents and skills in a central catalog and reference them by semantic version (e.g., “summarizer@2.3”). Build flows as directed acyclic graphs (DAGs) so you can visualize upstream/downstream impact and pause a node without collapsing the system.

Reliability comes from queues and exponential backoff retries, timeouts per hop, and idempotency keys so replays do not duplicate actions (emails, bookings, record updates). Add circuit breakers that fail fast when an upstream service degrades and define fallbacks—simulate, draft-only, or route to human. Policy validators and RBAC must guard sensitive actions; approvals trigger when inputs match risk conditions. Observability is non-negotiable: capture inputs, outputs, latencies, costs, and decisions in a single trace with correlation IDs for audit and debugging.

Promote changes with feature flags and canary cohorts; keep a kill-switch per agent and an emergency rollback plan. At TPG, we treat multi-agent work as governed orchestration—autonomy and dependencies are managed per workflow, segment, and region. Why TPG? Our consultants implement guardrail-first agent patterns across major MAP/CRM stacks with production-grade tracing and governance.