41 — Idempotent Tool Execution
Make repeated attempts at one business operation converge on one recorded effect, even when agents, queues, or networks deliver the request more than once.
Resilience, Reliability & Failure Handling
Make failure a designed part of the system. Recover work safely and keep useful service available.
What happens when a dependency fails?
- Bound work with deadlines and budgets.
- Separate transient failures from permanent errors.
- Make recovery safe to repeat.
42 — Circuit Breakers (Resilience4j)
Stop sending ordinary traffic to an unhealthy dependency, probe recovery deliberately, and protect the rest of the agent workflow from repeated failure.
43 — Self-Correction Loops
Repair specific validated defects in an agent’s work through bounded feedback, preserving accepted facts and preventing repeated unsafe actions.
44 — Human-in-the-Loop (HITL)
Place accountable human decisions at explicit workflow boundaries, presenting sufficient evidence and preserving approval scope through execution.
45 — Graceful Degradation
Preserve the most valuable safe subset of service during dependency failure, making reduced capability and evidence freshness explicit to users.
46 — Max Iterations Fallback
Terminate agent loops predictably when iteration, time, cost, or progress limits are reached, returning an honest partial outcome and a controlled next step.
47 — Fallback Prompt Strategies
Use evaluated alternate prompt strategies for specific recoverable failure classes while preserving policy, evidence requirements, and response contracts.
48 — Dead-Letter Queues (DLQ) for Agents
Quarantine repeatedly failing agent jobs with enough context for diagnosis and controlled replay, keeping poison work from blocking healthy processing.
49 — Timeout Policies
Allocate end-to-end deadlines across queueing, inference, tools, retries, and finalization so work stops predictably and uncertain side effects are reconciled.
50 — Exponential Backoff with Jitter
Retry transient failures with bounded exponential delays and randomized timing, preserving operation identity and respecting the remaining task deadline.
51 — State Rollbacks
Restore a coherent workflow checkpoint while explicitly compensating business effects that a database rollback cannot undo.
52 — Model Redundancy / Multi-Provider Failover
Continue eligible model work through independent providers without silently changing capability, privacy, or output contracts.
53 — Health Check Endpoints for Agents
Separate process survival, traffic readiness, and end-to-end agent capability so health automation makes the correct recovery decision.
54 — Memory Leak Mitigation in Long-Running Agents
Bound retained state and resource lifetimes so long-running agents remain stable as conversations, tools, and tenants accumulate.
55 — Distributed Tracing (OpenTelemetry)
Connect agent decisions, retrieval, model calls, and tool effects into one causal execution record using interoperable trace context.
56 — Load Balancing LLM Requests
Distribute inference work using token demand, queue pressure, capability, and cache locality instead of treating all requests as equal.
57 — Crash-Safe Memory
Persist agent progress and effect receipts so a replacement worker can resume safely after an abrupt process or machine failure.
58 — Partial Success Management
Represent completed, failed, and unknown sub-results explicitly so a workflow can deliver useful work without misrepresenting completion.
59 — Cascade Failure Prevention
Contain overload and dependency failures so one slow tool or model does not consume the capacity of an entire agent platform.
60 — Chaos Engineering for AI
Test explicit resilience hypotheses by injecting controlled infrastructure and AI-specific faults, then measuring whether workflow invariants survive.