1.4 — Workflow Enforcement and Handoff
CCAR-F Domain 1 · Task 1.4
What You Need to Know
Some agent workflows have steps that must run in a set order. Prompt-based guidance sets that order with a system prompt instruction. Programmatic enforcement sets it in code: hooks, prerequisite gates or code-level checks that physically block downstream tools until their prerequisites complete.
Prompt Guidance Versus Programmatic Enforcement
A prompt instruction works most of the time (perhaps 90-95%). It still has a non-zero failure rate because the model is probabilistic. A gate works every time — it is code, so it is deterministic.
Decision rule: If a single failure would cause financial loss, a security breach or a compliance violation, use programmatic enforcement.
- Financial operations (refunds, transfers, payments) → programmatic
- Security operations (identity verification, access control) → programmatic
- Compliance operations (AML checks, regulatory requirements) → programmatic
- Low-stakes operations (formatting, style) → prompt guidance is acceptable
How a Prerequisite Gate Works
- The gate checks whether a prior condition has been met for this session.
- If yes, the tool executes normally.
- If no, the tool returns an error telling the model to complete the prerequisite first.
The model cannot bypass the gate.
Subagent Lifecycle Hooks
- SubagentStart: fires when a subagent is spawned. Can add
additionalContext. Cannot block. - SubagentStop: fires when a subagent finishes. Can validate output (exit code 2 keeps the subagent running). Cannot rewrite the result.
Subagents can define their own hooks in frontmatter, scoped to their lifetime.
Multi-Concern Request Handling
- Decompose the request into distinct items.
- Investigate each in parallel using shared context.
- Synthesise a unified resolution that addresses all items in a single response.
Structured Handoff Protocols
When escalating to a human, the handoff summary must be self-contained and include:
- Customer ID
- Conversation summary
- Root cause analysis
- Refund amount (if applicable)
- Recommended action
Exam Traps
- Enhanced system prompt / few-shot as the fix for high-stakes compliance failures
- Handling multi-concern requests sequentially across separate replies
- Incomplete handoff summaries
Check your understanding
Practice Scenario
Production data shows that in 8% of cases an agent processes refunds without verifying account ownership. The system prompt already tells it to check first. What is the most appropriate fix?
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Sources
- Intercept and control agent behavior with hooks, Claude Code Docs
- Configure permissions, Claude Code Docs
- Handle approvals and user input, Claude Code Docs
- Building effective AI agents, Anthropic