Self Healing Agents

# Self-Healing Agents

Part of [Agent Skills™](https://github.com/itallstartedwithaidea/agent-skills) by [googleadsagent.ai™](https://googleadsagent.ai)

## Description

Self-Healing Agents are autonomous systems that detect their own failure modes and self-correct without human intervention. In production environments, agent failures are not exceptional — they are expected. Network calls timeout, APIs return unexpected schemas, models hallucinate confidently, and tool outputs violate assumptions. The difference between a prototype and a production agent is the ability to recover gracefully from every category of failure.

This skill encodes the self-healing patterns developed for the Buddy™ agent at [googleadsagent.ai™](https://googleadsagent.ai), where autonomous Google Ads analysis must complete reliably even when upstream APIs change, rate limits are hit, or model outputs contain structural errors. The system operates on a detect-diagnose-repair cycle that mirrors biological immune responses: identify the pathogen, classify the threat, and deploy the appropriate countermeasure.

Self-healing is not merely retry logic. It encompasses error classification, strategy mutation (retrying with a different approach rather than the same one), fallback model selection, output validation with automatic repair, and graceful degradation when full recovery is impossible. Agents built with these patterns achieve 99%+ task completion rates in production.

## Use When

- Building agents that must operate autonomously without human oversight
- Tool calls or API integrations are unreliable or subject to rate limits
- Model outputs must conform to strict schemas and occasionally don't
- Long-running workflows cannot afford to fail mid-execution
- You need to maintain SLA commitments for agent-powered features
- The agent must handle novel error types it hasn't encountered before

## How It Works

```mermaid
graph TD
    A[Agent Action] --> B[Output Validation]
    B -->|Valid| C[Continue Execution]
    B -->|Invalid| D[Error Classifier]
    D --> E{Error Type}
    E -->|Transient| F[Retry with Backoff]
    E -->|Structural| G[Mutate Strategy]
    E -->|Model Error| H[Fallback Model]
    E -->|Unrecoverable| I[Graceful Degradation]
    F --> J{Retry Budget Remaining?}
    J -->|Yes| A
    J -->|No| G
    G --> K[Modified Prompt/Approach]
    K --> A
    H --> L[Alternative Model Execution]
    L --> B
    I --> M[Partial Result + Error Report]
```

The self-healing cycle activates whenever output validation detects an anomaly. The error classifier categorizes the failure into one of four types: transient errors (network timeouts, rate limits) are retried with exponential backoff; structural errors (schema violations, missing fields) trigger strategy mutation where the agent modifies its approach; model errors (hallucinations, refusals) invoke fallback model selection; and unrecoverable errors trigger graceful degradation that returns the best partial result with a clear error report.

## Implementation

**Error Classification Engine:**

```typescript
enum ErrorType {
  Transient = "transient",
  Structural = "structural",
  ModelError = "model_error",
  Unrecoverable = "unrecoverable",
}

interface ClassifiedError {
  type: ErrorType;
  message: string;
  retryable: boolean;
  suggestedStrategy: string;
}

function classifyError(error: unknown, context: ExecutionContext): ClassifiedError {
  if (error instanceof NetworkError || error instanceof RateLimitError) {
    return {
      type: ErrorType.Transient,
      message: String(error),
      retryable: true,
      suggestedStrategy: "exponential_backoff",
    };
  }
  if (error instanceof SchemaValidationError) {
    return {
      type: ErrorType.Structural,
      message: `Schema violation: ${error.path} — ${error.me