Key Takeaways
- Model agent workflows as state machines with defined states, transitions, and guards for predictable, debuggable behavior.
- Error recovery is the most critical orchestration feature: retry, fallback, escalate, or fail gracefully depending on error type and task criticality.
- Multi-agent coordination patterns: pipeline (sequential), fan-out (parallel), fan-in (aggregate), and supervisor (manager delegates).
- State persistence enables resumption: store agent state durably so long-running tasks survive failures and can be replayed for debugging.
- Resource management prevents cost overruns: limit concurrent executions, rate-limit API calls, and manage LLM token budgets.
State Machine Pattern for Agent Workflows
| State | Description | Transitions To | Guard Conditions |
|---|---|---|---|
| Planning | Agent reasons about next step | Executing, Complete | Plan is valid |
| Executing | Tool call in progress | Observing, Error | Tool call initiated |
| Observing | Processing tool results | Planning, Complete | Results received |
| Waiting | Awaiting human input | Planning | Human response received |
| Error | Handling failure | Planning, Failed | Recovery strategy available |
| Complete | Task finished successfully | (terminal) | Success criteria met |
| Failed | Task could not be completed | (terminal) | Recovery exhausted |
State machines make agent behavior predictable and debuggable. Every transition is logged, guards prevent invalid state changes, and the current state is always known. This is far more reliable than unstructured agent loops.
Error Recovery Strategies
| Error Type | Recovery Strategy | Max Retries | Escalation |
|---|---|---|---|
| Tool timeout | Retry with backoff | 3 | Try alternative tool |
| Invalid parameters | Rephrase and retry | 2 | Ask user for clarification |
| API rate limit | Wait and retry | 5 | Queue for later |
| Permission denied | No retry | 0 | Escalate to human |
| Unknown error | Retry once | 1 | Log and escalate |
| LLM hallucination | Re-prompt with constraints | 2 | Use simpler model |
For long-running agent tasks (10+ minutes), implement checkpointing. Save the agent's state every 5-10 steps. If the process crashes, resume from the last checkpoint instead of starting over. This is especially important for expensive multi-step workflows.
Multi-Agent Coordination Patterns
| Pattern | Description | Use Case | Complexity |
|---|---|---|---|
| Pipeline | Agent A output feeds Agent B input | Content creation, data processing | Low |
| Fan-out | Split task into parallel independent subtasks | Research, data gathering | Medium |
| Fan-in | Aggregate results from parallel agents | Synthesis, decision making | Medium |
| Supervisor | Manager agent delegates and reviews | Complex projects | High |
| Blackboard | Agents read/write shared state | Collaborative problem solving | High |
Start with the simplest pattern that fits your workflow. Pipeline handles 70% of multi-agent use cases. Add fan-out/fan-in when you need parallelism. Use supervisor only when dynamic task assignment is required.
Monitoring and Resource Management
- Concurrent execution limits: cap the number of agents running simultaneously to prevent resource exhaustion and cost overruns
- Token budgets: set maximum token consumption per agent task. When the budget is exhausted, the agent summarizes progress and stops.
- Timeout enforcement: set maximum execution time per task (typically 5-30 minutes). Kill runaway agents that exceed limits.
- Metrics to track: task completion rate, average execution time, error rate, token consumption, and cost per task
- Alerting thresholds: alert on completion rate drops (below 90%), cost spikes (2x daily average), and error rate increases (above 5%)
For scalable agent orchestration, use message queues (Redis, RabbitMQ, SQS) to decouple request handling from agent execution. This enables async processing, automatic retries, horizontal scaling, and graceful handling of traffic spikes.
Frequently Asked Questions
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