Playbook
Agentic Workflow Design Playbook
Agentic workflows need clear task ownership, tool permissions, review gates, telemetry, and human escalation. Without those controls, agents create operational risk.
Best reader
Teams planning multi-agent software or operational AI workflows
Outcome
A safer agent workflow design that separates acceleration from unchecked autonomy.
Use this sequence
Define what each agent can read, draft, and execute.
Set review gates for risky or irreversible actions.
Create escalation paths for uncertainty and blocked tasks.
Measure acceptance rate, rework, and time saved.
Keep humans accountable for final decisions.
Start with task boundaries
An agent should have a narrow job, a clear input, and a defined output. Broad agents are harder to review.
Task scope
Allowed tools
Blocked actions
Output format
Design human review before automation
Human review should be built into the workflow before agents are allowed to perform meaningful work.
Approval trigger
Reviewer role
Evidence required
Escalation path
Measure agent quality
Telemetry turns agent output into an observable delivery system instead of a black box.
Acceptance rate
Rework rate
Cycle time
Failure categories
Start in 60 seconds
Turn this checklist into a scoped plan.
Answer a few questions and get an AI-assisted, architect-reviewed scope, cost range, and timeline for your software project.
Frequently asked questions
Can AI agents operate without human review?
Low-risk tasks can be automated, but high-impact or irreversible work should require human review.
What is the most important agent metric?
Acceptance rate is a useful start, but it should be read with rework, cycle time, and risk level.