One agent retries a timed-out call with no backoff. Another accepts a fabricated result as fact. A third updates shared state after the workflow has already moved on. If that sounds like a distributed-systems problem, it is. The job titles changed. The failure modes did not.

Multi-agent systems inherit partial failures, duplicate execution, stale state, backpressure, and cascading outages. This session maps the patterns engineers already use in distributed systems onto agent-to-agent workflows: idempotency keys for safe retries, circuit breakers for failing dependencies, compensating actions for partially completed work, and explicit ownership for shared state.

A small multi-agent pipeline provides the walkthrough. First, one failed call triggers retries and inconsistent state across the workflow. Then we contain the same failure with bounded retries, idempotent operations, circuit breaking, and observable handoffs. We will also compare orchestration with choreography and show where each makes recovery easier—or harder.

Attendees will leave able to explain why multi-agent workflows fail like distributed systems, design safer agent-to-agent calls, choose a coordination model based on failure and recovery needs, and reason about consistency without pretending every agent sees the same world at the same time.

No framework is required. The goal is to apply proven engineering patterns before a small agent failure becomes a system-wide one.

Audience: mid-to-senior engineers and architects. Tracks: AI Agents & Workflows, Architecture, Programming Principles. Format: adaptable to 30, 45, or 60 minutes with a code walkthrough. Vendor-neutral.