Structures multi-step agent tasks with clear inputs, outputs, fallbacks, and handoff protocols so agents reliably complete complex workflows instead of stopping at the first blocker.
Use cases
- Multi-step automation
- Fallback design
- Handoff protocols
- Reliability engineering
Key features
- Define task boundaries and inputs
- Design step outputs and error triggers
- Add human checkpoints for risky steps
- Test with failure injection
Related
Related
3 Indexed items
Multi-agent orchestration
Coordinates multiple AI agents on shared tasks with explicit handoffs, conflict resolution, and result aggregation—so parallel work stays coherent instead of fragmenting into inconsistent outputs.
Dispatching parallel agents
Splits embarrassingly parallel work—research chunks, file batches, or independent modules—across agents with crisp handoffs back to a single integrator.
Subagent-driven development
Coordinates subagents on slices of a larger plan—ideal when one thread would be too slow but you still need a single accountable integration point.