Multi-agent systems are compelling in theory and difficult in practice. The promise: complex tasks decomposed across specialised agents, each doing what it does best, with a coordinator orchestrating the whole. The reality: debugging cascading failures, tracing which agent produced which output, and dealing with agents that confidently hallucinate into each other's contexts.
We've learned a lot from running multi-agent workflows in production. Here are the patterns that work.
Explicit routing over implicit delegation
Early versions of our multi-agent framework used an LLM to decide which agent to call next. This produced non-deterministic routing that was nearly impossible to debug. We replaced it with explicit routing tables: if the task matches this pattern, call this agent. Routing logic is code, not a prompt.
Structured handoffs
When one agent hands off to another, it produces a structured handoff object — not free-form text. The handoff object has a typed schema: what the upstream agent found, what it's uncertain about, and what the downstream agent should focus on. This dramatically reduces context pollution and makes the workflow auditable.
Error recovery as a first-class concern
In any sufficiently complex workflow, agents will fail. The question is what happens next. We build every workflow with explicit retry logic, fallback paths, and human-in-the-loop escalation for cases where automated recovery isn't possible. An agent that fails silently is worse than one that fails loudly.
Observability from day one
Every step in every workflow should emit a trace event with its input, output, latency, and token usage. Without this, debugging production failures is guesswork. We built workflow observability as a core platform feature, not an afterthought.