Beyond 'Vibes': The Power of Metric-Vector Decision Logic

In the world of autonomous agents, "vibes" are no longer enough. Most agents today rely on a loosely defined "thought process" where the LLM simply guesses the next action based on previous logs. This leads to drift, regression, and failure.

The Problem: Qualitatitive Uncertainty

When an agent tries to optimize code or fix a bug, how does it know it's making progress?

• Is the new code faster?
• Is it more accurate?
• Does it pass the same tests?

Without quantitative grounding, the agent is effectively flying blind.

The Solution: The Remoroo Metric-Vector

Remoroo treats every experiment as a search for an optimal point in a Metric-Vector Space.

Instead of a single "score", Remoroo tracks a vector of metrics:

• Accuracy: Does it still work?
• Latency: Is it fast enough?
• Cost: How many tokens did it take?
• Reliability: Does it pass edge-case stress tests?

Deterministic Feedback Hooks

Every time a Remoroo agent applies a patch, it triggers a Validation Hook. This hook executes the user's validation script and returns a precise measurement of the change's impact.

If the change regresses any critical metric beyond a defined threshold, the agent rejects and rolls back. It doesn't "think" about why it might be okay; it follows the data.

Monotonic Improvement

This deterministic loop ensures that your experiment results move in only one direction: Towards the Goal. By removing qualitative uncertainty, Remoroo converts agentic "chaos" into professional engineering.

Read more about how we implement these hooks in our CLI Reference.