I've been using Claude Code heavily for months. It's great for velocity, but I kept hitting the same problems:
- Agent hallucinates file paths that don't exist
- Claims "tests pass" without running them
- Same errors recurring across sessions
- No way to catch failures that aren't crashes
The tools exist to catch crashes. Nothing exists to catch semantic failures - when the agent confidently gives wrong answers.
So I built Task Orchestrator - an MCP server that adds an "immune system" to Claude Code:
1. Semantic failure detection - catches hallucinations, not just crashes
2. ML-powered learning - remembers failure patterns, warns before similar prompts
3. Human-in-the-loop - queues high-risk operations for approval
4. Cost tracking - see exactly what you're spending
5. Self-healing circuit breakers
The math problem: at 95% per-step reliability, a 20-step workflow has only 36% success rate. That's not a bug - it's compound probability.
Technical details:
- 680+ tests
- Provider-agnostic (works with any LLM)
- MCP native for Claude Code
- MIT licensed
What features would you want to see that would improve your AI agent workflows?
I've been using Claude Code heavily for months. It's great for velocity, but I kept hitting the same problems: