Agent Orchestration & Monitoring
Run longer AI workflows reliably: workflow patterns, routing and model selection, parallel work, state and checkpoints, human-in-the-loop approvals, error recovery, multi-agent systems, scheduling and triggers, cost control, and production monitoring.
Agent Orchestration & Monitoring
Run longer AI workflows reliably: workflow patterns, routing and model selection, parallel work, state and checkpoints, human-in-the-loop approvals, error recovery, multi-agent systems, scheduling and triggers, cost control, and production monitoring.
In one line
Orchestration is how AI steps, tools and people are connected into a workflow that runs reliably; monitoring is how you know it keeps working. Start with a simple workflow and add agents only where they are needed.
The 8 classes
Workflows vs agents: start simple
Know when a fixed workflow is enough and when an agent is worth it.
- Workflows are predictable; agents are flexible.
- Start simple; add autonomy only when needed.
- Measure with evals before adding complexity.
Core patterns: chaining, routing and parallelisation
Use the three most common orchestration patterns.
- Chain for sequences, route for variety, parallelise for speed.
- Routing also picks the right model for cost.
- Check outputs between steps.
Orchestrator-workers and evaluator-optimizer
Use the two most powerful agentic patterns safely.
- Orchestrator plans; workers execute; orchestrator combines.
- Evaluator-optimizer loops on a rubric with a step cap.
- Limits on steps, time and cost are mandatory.
State, checkpoints and long-running work
Make workflows resumable and traceable.
- Track state per run.
- Checkpoint after major steps.
- Make actions idempotent; cache reusable results.
Human-in-the-loop, approvals and error recovery
Put people at the right points and recover from failures safely.
- Approvals before consequential actions.
- Retry with limits; fall back; escalate.
- Never fail silently: alert an owner.
Multi-agent systems
Know when splitting work across agents helps, and how to coordinate them.
- Multi-agent helps for large parallel work.
- Clear roles, shared state, a coordinator.
- Prove the benefit with evals.
Triggers, scheduling and cost control
Run workflows automatically and within budget.
- Choose scheduled, event-driven or on-demand triggers.
- Set budgets and alerts.
- Measure cost per successful task.
Monitoring, observability and the final project
Know when a workflow breaks or degrades, and fix it fast.
- Monitor success, quality, latency, cost, safety, drift.
- Trace every run for debugging.
- Ship with alerts and an owner.
Interview questions
Workflow or agent?
Workflow when steps are known and repeatable; agent when steps can't be predicted. Start simple and prove the agent is better with evals.
Explain routing.
Classify each request and send it to the appropriate workflow or model, e.g. cheap model for FAQs, strong model for complex cases.
What is orchestrator-workers?
A lead model breaks a task into sub-tasks, delegates to workers and synthesises their results.
How do you make long workflows reliable?
State tracking, checkpoints, idempotent actions, retries with limits, approvals, alerts and monitoring.
When do you need multiple agents?
Large parallel work needing different tools or context, and only when evals show it beats a single agent.
How do you control AI workflow cost?
Budgets and alerts, routing to smaller models, caching, trimmed context, step and retry caps, and tracking cost per successful task.
What would you monitor in production?
Success rate, sampled quality scores, latency, cost, safety events and input drift, with owners and alerts.
Where do humans fit?
Before consequential actions, on low confidence or conflicts, and in sampled quality review.
