Concept lesson

Agent Failure Recovery

Design retries, compensation, escalation, and safe termination around typed agent failure states.

lesson
Freshness: current14 min read
Mastery
not started · 0%

Learning outcomes

  • Explain the operating model behind Agent Failure Recovery.
  • Evaluate trade-offs and failure modes for Agent Failure Recovery.
  • Apply Agent Failure Recovery to a production decision.

Mental model

Recovery is a state transition, not another prompt: classify the failure, decide whether the action is safe to repeat, restore a known checkpoint, and either retry, compensate, or escalate.

Problem boundary
Evidence and state
Deterministic control
Probabilistic decision
Verification and feedback
Conceptual teaching model synthesized from:Trustworthy Agents in PracticeSite Reliability Engineering

Learning outcomes

  • Explain the mechanism and ownership boundaries behind Agent Failure Recovery.
  • Compare the main design alternatives and their operational trade-offs.
  • Diagnose common failures and select evidence for a production decision.

Theory

Failures should be typed as transient, deterministic, policy, dependency, ambiguous side effect, or exhausted budget. Recovery policy belongs in deterministic orchestration and must preserve idempotency keys, attempt limits, and audit history.

Trade-offs

Automatic retry improves resilience for transient faults but amplifies duplicate side effects and cost when failure classification is wrong. Human escalation is slower but necessary for ambiguous or consequential state.

Failure modes and misconceptions

Retrying non-idempotent tools; losing the original failure; resetting budgets during recovery; compensating without evidence; and continuing after policy denial.

Decision scenario

A payment tool times out after submission. Determine whether to retry, query state, compensate, or escalate without charging twice.

Reflect before revealing the guide

What evidence must be collected before retrying a tool whose first result is ambiguous?

Primary sources

  • anthropic-trustworthy-agents
  • sre-book

Evidence assessment

Theory and decision mastery

not-started · 0%
theory0%
decision0%
activityNot mapped
projectNot mapped
1. Which statement best captures the operating model for Agent Failure Recovery?
2. What is the strongest way to validate a production decision involving Agent Failure Recovery?
3. Which practice most often creates hidden risk around Agent Failure Recovery?

Decision scenario

A production team must adopt Agent Failure Recovery while meeting quality, latency, security, and operating constraints.

Which decision process is most defensible?

Relationships

prerequisite
Agent Control Loop

Agent Failure Recovery builds on Agent Control Loop.

related
AI Risk Governance

Agent Failure Recovery informs governed production decisions and review evidence.

Primary sources