agent-reliability-and-guardrails

What problem does it solve?

This Skill provides AI engineers with a robust framework for building autonomous agents, addressing common reliability issues and potential failures.

Core Features & Use Cases

• Budget Management: Implement five critical budgets (iteration, tool calls, tokens, wall-clock, cost) to prevent agent runaway.
• Termination Conditions: Define explicit success criteria, no-progress detection, and repetition prevention to safely exit agent loops.
• Guardrails: Enforce input/output/action checks to prevent data corruption and unauthorized actions.
• Sub-Agent Orchestration: Safely manage nested sub-agents with bounded depth, no self-spawning, isolated contexts, budget inheritance, and fan-out.
• Verification: Always verify tool outputs and success claims to prevent false positives.
• State Management: Implement checkpointing, resuming, and idempotent replay to ensure long-running agent stability.
• Observability: Instrument the agent for traceability, error detection, and performance monitoring.
• Failure Modes: Identify and mitigate common failure modes like runaway loops, hallucinations, and false completion claims.
• Use Case: For building a robust AI assistant that operates within predefined constraints and reliably completes tasks without human intervention.

Quick Start

Use the agent-reliability-and-guardrails skill to evaluate the reliability of your AI agent with the 'evaluate_agent' command.