AI Agent Workflows Cheat Sheet

Updated 2026-03-06

Core Workflow Patterns

Pattern	Use Case	Example
Plan-then-execute	Multi-step tasks with dependencies	Agent plans research steps before running tools
Retrieve-then-generate	Grounding responses in source data	Pull docs from vector store before final answer
Verify-before-commit	Prevent bad writes	Validate output schema before DB update
Human-in-the-loop	High-risk actions	Require approval before external email send

Reliability Controls

Control	Why It Matters	Example
Retry with backoff	Survive transient failures	`sleep = 2^n + jitter`
Timeouts	Prevent hung runs	Cancel task after 90s
Circuit breaker	Avoid cascading failures	Pause tool after repeated errors
Idempotency key	Avoid duplicate side effects	One invoice write per request id

Governance Checklist

Item	Minimum Standard	Example
Tool permissions	Least privilege	Read-only access for report agents
Audit logs	Action traceability	Store prompt, tool call, output, decision
Escalation path	Safe failure handling	Route failed tasks to ops queue
Data boundary	Compliance and privacy	Redact PII before model calls

Usage Notes

Start with one workflow that is repetitive, measurable, and low risk. Add autonomy gradually: suggestion mode first, auto-execution second. Keep a rollback path for every workflow.

Common Failure Patterns

Teams often over-automate too early. The first version should not write to critical systems without approval gates. Another common issue is weak observability: if you cannot trace tool calls and decisions, debugging becomes guesswork. Build logging and escalation into day one architecture.

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