What Fallback Systems Are Needed for AI Agents?
Design safe failure modes: degrade, retry, escalate, or stop—backed by approvals, audit logs, and disaster recovery.
Executive Summary
Direct answer: Agents need layered fallbacks: retries with backoff and idempotency; circuit breakers and timeouts; draft-only/readonly modes; human-in-the-loop escalation; safe defaults (holdout or control variant); feature flags, canaries, and kill switches; persistent queues; policy validators; observability and audit logs; backups and disaster recovery. Each fallback must be pre-authorized, tested, and measurable.
Guiding Principles
Fallback Design: Do / Don’t
| Do | Don’t | Why |
|---|---|---|
| Use queues, retries, and timeouts per hop | Retry endlessly without backoff | Prevents thundering herds, controls load |
| Add circuit breakers with health checks | Call degraded services repeatedly | Fail fast, save budget |
| Keep draft/readonly and simulation modes | Disable all assistance on failure | Maintain partial value safely |
| Guard with policy validators and quotas | Allow unlimited actions in errors | Contain blast radius |
| Instrument traces, alerts, and reason codes | Hide errors from operators | Rapid diagnosis and auditability |
Fallback Playbook (Activation Paths)
| Trigger | Fallback Mode | Action | Owner | Exit Criteria |
|---|---|---|---|---|
| Upstream timeout or 5xx | Circuit break | Open breaker; use cached/last-known-good | Platform Owner | Health checks pass N times |
| Policy validator fail | Assist-only | Produce draft; request approval | Channel Owner | Manual approval or fixed inputs |
| Budget or rate limit hit | Throttle | Slow/queue; prioritize high-value tasks | RevOps | Window resets or extra quota granted |
| Experiment underperforming | Auto-rollback | Promote control; stop variant | Governance Board | Root cause fixed; re-test |
| Security/compliance event | Kill switch | Disable actions; preserve logs | Security | Incident closed; sign-off recorded |
What “Good” Looks Like (Expanded)
A resilient agent has multiple ways to keep users safe and productive during failure. Network or vendor issues trigger timeouts, retries with exponential backoff, and circuit breakers that switch to cached or control content. Business risks invoke draft-only or simulation modes that still provide guidance but block live actions until approvals arrive. Queues, idempotency keys, and deduplication protect downstream systems from duplicate sends or bookings. Feature flags and canaries allow you to roll out or roll back behavior without redeploying.
Every fallback should leave a precise trail—inputs, decisions, reason codes, costs, and correlation IDs—so operators can diagnose issues quickly. DR planning covers config/version backups, secrets rotation, and region failover where applicable. Finally, rehearse: run game-days to practice breaker openings, rollbacks, and kill-switch drills. Why TPG? We implement guardrail-first agent architectures—contracts, validators, observability, and DR—across enterprise MAP/CRM and cloud stacks.
Resilience Metrics & Benchmarks
| Metric | Formula | Target/Range | Stage | Notes |
|---|---|---|---|---|
| MTTR | Avg time to recover | Trending down | Operate | Practice runbooks |
| Auto-rollback success | Successful rollbacks ÷ attempts | ≥ 95% | Operate | No residual side-effects |
| Duplicate action rate | Duplicates ÷ total actions | 0% | Execute | Idempotency required |
| Breaker accuracy | Correct opens ÷ total opens | ≥ 90% | Guard | Tune health thresholds |
| Alert fatigue | Actionable alerts ÷ total alerts | ≥ 70% | Observe | Deduplicate, route by severity |
Frequently Asked Questions
Switch to assist/draft mode with policy validators while breakers open and retries run in the background, preserving value without risk.
At the orchestrator level with RBAC, logging, and confirmation prompts; it should disable actions but keep read-only diagnostics.
Yes. Breakers handle technical failures; approvals govern business risk on sensitive actions like publishing or budget changes.
Run game-days that simulate vendor outages, latency spikes, and policy violations; measure MTTR and rollback time to improve runbooks.
Caches provide last-known-good content or decisions during transient failures; expire aggressively to avoid stale outputs.