Single-agent workflows get you far. One agent that plans, codes, and tests can handle many tasks. But as systems grow in complexity, specialization pays off: an agent focused on research, another on design, another on implementation, another on review. Each gets better at its domain. The challenge is orchestrating them—and designing the handoffs so the whole is greater than the sum of parts.

The Multi-Agent Landscape

Specialist agents.
Agents tuned for specific tasks: user research synthesis, UI design, backend implementation, security review. Each has different prompts, different tools, different output formats. The orchestration layer decides which agent runs when and how output flows between them.

Orchestrator patterns.
Who coordinates? Options: a meta-agent that delegates to specialists; a human who hands off between agents; a workflow engine that routes based on rules. Each has tradeoffs. The meta-agent can scale but may make poor delegation decisions. The human has better judgment but becomes a bottleneck. The workflow engine is predictable but inflexible.

Handoff design.
When Agent A finishes, what does Agent B receive? Context summary? Raw output? Structured artifacts? The handoff format determines whether the next agent can do its job. Poor handoffs—too much noise, too little structure—degrade the chain.

Design Principles

1. Clear boundaries.
Each agent should have a well-defined scope. “Research agent produces a synthesis doc. Design agent produces wireframes. Code agent produces implementation.” Overlap creates confusion; gaps create failure. Boundaries should be explicit and enforced.

2. Structured handoffs.
Define the contract: what format, what fields, what’s required vs. optional. Treat handoffs like API boundaries—versioned, documented, validated. Unstructured handoffs (“here’s a blob of text”) don’t scale.

3. Human checkpoints.
Multi-agent chains can compound errors. Agent A’s mistake flows to B, which amplifies it. Build in human checkpoints at critical points: after research (before design?), after design (before code?), before merge. The human validates and can redirect the chain.

4. Observability across the chain.
When something goes wrong, you need to trace it. Which agent produced what? What was the input to each step? Log the chain. Make it debuggable.

Multi-agent orchestration is where agentic systems get powerful—and complex. Invest in the boundaries and handoffs early. They’ll pay off as the system grows.