Resilient Agent Architecture: Solving the Loop Problem with Caselets
Autonomous agents often get stuck. Whether it's an infinite retry loop or a logical dead-end, traditional "vibes-based" decision logic fails when stakes are high.
The Problem: Volatile System State
Most agents operate on a stateless basis. When a command fails, they try to "think" of a new approach in a vacuum. This leads to:
- Redundant Retries: Running the same failed command multiple times.
- Logic Drift: Moving further away from the goal with each turn.
- Infinite Loops: Getting stuck in a circle of repetitive actions.
The Solution: Case-Based Reasoning (CBR)
Remoroo introduces Caselets. Instead of relying solely on the LLM's short-term memory, Remoroo records every success and failure as a "Case".
When an agent encounters a failure, it doesn't just ask the LLM what to do. It queries the Caselet Database:
- Has this specific error (e.g.,
ModuleNotFoundError) happened before? - What was the successful resolution in previous experiments?
- What actions were tried and failed in this run?
By grounding the LLM in historical and current evidence, Caselets provide a "survival guide" that ensures progress is always monotonic.
Monotonic Progress
In Remoroo, every turn is evaluated against a Metric Vector. If a proposed change doesn't move the success criteria forward, it's flagged or rolled back. This deterministic feedback loop, combined with Caselet memory, makes Remoroo the most resilient engine for high-performance AI research.
Learn more about our Metric-Vector decision logic in our Architecture Docs.