Agent Reliability Patterns

After shipping AI agents at Google, Meta, and in robotics, I've learned that production reliability comes down to five core patterns. Most teams skip these and wonder why their demos don't scale.

The Five Patterns

1. Graceful Degradation

When the model fails, the system should degrade to a simpler behavior, not crash. Design fallback chains: GPT-4 → GPT-3.5 → rule-based → human handoff.

Implementation: Define degradation levels in your agent config. Each level should have clear success criteria and automatic promotion/demotion logic.

2. Bounded Autonomy

Agents need guardrails. Define explicit boundaries for what the agent can and cannot do. Use allowlists, not blocklists.

Implementation: Create an "action registry" with permissions, rate limits, and approval requirements. Every agent action must be registered.

3. Observable State

You can't debug what you can't see. Every agent decision should be traceable, with clear reasoning chains and intermediate states logged.

Implementation: Structured logging with decision IDs, reasoning traces, and state snapshots. Use your existing observability stack (Datadog, Honeycomb, etc.).

4. Human-in-the-Loop Control Surfaces

Build UIs for humans to intervene, not just monitor. Operators need to be able to pause, override, and teach the agent in real-time.

Implementation: Create operator dashboards with pause/resume, manual override, and feedback collection. Make intervention easy.

5. Incremental Rollout

Never ship agents to 100% of traffic on day one. Use feature flags, gradual rollout, and automatic rollback on quality degradation.

Implementation: Start at 1% traffic with strict quality gates. Double traffic weekly if metrics hold. Auto-rollback on SLO violations.

Real-World Example

At Meta, we launched Instagram Calling using these patterns:

• Started at 0.1% with manual approval for every call
• Degraded to audio-only when video quality dropped
• Logged every decision with full context
• Built operator tools for real-time intervention
• Reached 75% DAU adoption in 6 months with 99.9% reliability

Measuring Success

Track these metrics:

• Agent Intervention Rate (AIR): % of tasks requiring human intervention
• Degradation Frequency: How often fallbacks trigger
• Mean Time to Recovery (MTTR): How fast you recover from failures
• Task Completion Rate: % of tasks completed successfully

Target: AIR < 5%, MTTR < 5 minutes, Task Completion > 95%