Agent Self Correction

Agent Self Correction

Skill Profile

(Select at least one profile to enable specific modules)

• DevOps
• Backend
• Frontend
• AI-RAG
• Security Critical

Overview

AI agent self-correction mechanisms enable agents to detect errors, validate outputs, and automatically recover from failures. This includes validation loops, confidence scoring, iterative refinement, and recovery strategies to improve reliability.

Why This Matters

• Reliability: Agents can correct errors themselves without human intervention
• Quality: Output quality improves through iterative refinement
• Trust: Users are confident in results through confidence scoring
• Efficiency: Reduce unnecessary retry loops through smart recovery

Core Concepts & Rules

1. Core Principles

• Follow established patterns and conventions
• Maintain consistency across codebase
• Document decisions and trade-offs

2. Implementation Guidelines

• Start with the simplest viable solution
• Iterate based on feedback and requirements
• Test thoroughly before deployment

Inputs / Outputs / Contracts

• Inputs:
  • Task description or prompt
  • Expected output format (JSON, text, etc.)
  • Context information (facts, constraints)
  • Validation rules and thresholds
• Entry Conditions:
  • LLM API is accessible
  • Error detection patterns are defined
  • Recovery strategies are configured
• Outputs:
  • Validated and corrected outputs
  • Confidence scores
  • Error reports
  • Improvement metrics
• Artifacts Required (Deliverables):
  • Error detector implementation
  • Validation loop implementation
  • Recovery strategy definitions
  • Self-reflection system
• Acceptance Evidence:
  • Errors are detected and corrected
  • Validation loops converge
  • Confidence scores improve over iterations
  • Recovery strategies work correctly
• Success Criteria:
  • Error detection accuracy > 90%
  • Validation convergence rate > 95%
  • Confidence scores correlate with quality
  • Recovery success rate > 80%

Skill Composition

• Depends on: skill-architect
• Compatible with: task-decomposition-strategy
• Conflicts with: Infinite validation loops, over-correction
• Related Skills: skill-discovery-and-chaining

Quick Start

// 1. Set up error detection
const detector = new ErrorDetector()

// 2. Set up validation loop
const loop = new ValidationLoop()

// 3. Execute with self-correction
const result = await loop.executeWithValidation(
  () => llm.generate(task),
  (output) => {
    const errors = detector.detectErrors(output, context)
    return {
      isValid: errors.length === 0,
      errors: errors.map(e => e.message),
      warnings: [],
      confidence: 1.0 - (errors.length * 0.2),
    }
  },
  (output, errors) => llm.generate(`Fix: ${errors.join(', ')}\nOutput: ${output}`)
)
)

Assumptions / Constraints / Non-goals

• Assumptions:
  • Development environment is properly configured
  • Required dependencies are available
  • Team has basic understanding of domain
• Constraints:
  • Must follow existing codebase conventions
  • Time and resource limitations
  • Compatibility requirements
• Non-goals:
  • This skill does not cover edge cases outside scope
  • Not a replacement for formal training

Compatibility & Prerequisites

• Supported Versions:
  • Python 3.8+
  • Node.js 16+
  • Modern browsers (Chrome, Firefox, Safari, Edge)
• Required AI Tools:
  • Code editor (VS Code recommended)
  • Testing framework appropriate for language
  • Version control (Git)
• Dependencies:
  • Language-specific package manager
  • Build tools
  • Testing libraries
• Environment Setup:
  • .env.example keys: API_KEY, DATABASE_URL (no values)

Test Scenario Matrix (QA Strategy)

Technical Guardrails & Security Threat Model

1. Security & Privacy (Threat Model)

• Top Threats: Injection attacks, authentication bypass, data exposure

• Data Handling: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
• Secrets Management: No hardcoded API keys. Use Env Vars/Secrets Manager
• Authorization: Validate user permissions before state changes

2. Performance & Resources

• Execution Efficiency: Consider time complexity for algorithms
• Memory Management: Use streams/pagination for large data
• Resource Cleanup: Close DB connections/file handlers in finally blocks

3. Architecture & Scalability

• Design Pattern: Follow SOLID principles, use Dependency Injection
• Modularity: Decouple logic from UI/Frameworks

4. Observability & Reliability

• Logging Standards: Structured JSON, include trace IDs request_id
• Metrics: Track error_rate, latency, queue_depth
• Error Handling: Standardized error codes, no bare except
• Observability Artifacts:
  • Log Fields: timestamp, level, message, request_id
  • Metrics: request_count, error_count, response_time
  • Dashboards/Alerts: High Error Rate > 5%

Agent Directives & Error Recovery

(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)

• Thinking Process: Analyze root cause before fixing. Do not brute-force.
• Fallback Strategy: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
• Self-Review: Check against Guardrails & Anti-patterns before finalizing.
• Output Constraints: Output ONLY the modified code block. Do not explain unless asked.

Definition of Done (DoD) Checklist

• Tests passed + coverage met
• Lint/Typecheck passed
• Logging/Metrics/Trace implemented
• Security checks passed
• Documentation/Changelog updated
• Accessibility/Performance requirements met (if frontend)

Anti-patterns / Pitfalls

• ⛔ Don't: Log PII, catch-all exception, N+1 queries
• ⚠️ Watch out for: Common symptoms and quick fixes
• 💡 Instead: Use proper error handling, pagination, and logging

Reference Links & Examples

• Internal documentation and examples
• Official documentation and best practices
• Community resources and discussions

Versioning & Changelog

• Version: 1.0.0
• Changelog:
  • 2026-02-22: Initial version with complete template structure

Source: AmnadTaowsoam/CerebraSkills — distributed by TomeVault.