Self-Improving Platform

The platform that gets
smarter with every run.

Most AI tools give you the same output on day 100 as on day 1. Orqista captures what works, fixes what doesn't, and accumulates institutional knowledge — automatically.

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The self-improving feedback loop

1

Expert runs a job

An expert agent executes a mini-job using its current skill guidance.

2

Post-job assessment

A lightweight assessor (Haiku) evaluates the output and proposes skill improvements.

3

You review & approve

Proposed improvements are surfaced in the dashboard. You accept, modify, or dismiss.

4

Skills evolve

Approved changes are versioned and immediately active for the next run.

Three ways skills evolve

Every evolution is classified by how it came to be. This classification determines how it's stored, versioned, and prioritized in future runs.

FIX
Triggered by: Failure rate ≥ 40% (min. 5 uses)

In-place repair

When a skill underperforms — too many errors, timeouts, or low-quality outputs — a FIX replaces the problematic sections with corrected guidance based on the observed failure patterns.

An expert kept generating TypeScript without proper null checks. FIX adds an explicit null-safety checklist to the skill.
DERIVED
Triggered by: Exceptional success pattern detected

New skill variant

When an expert discovers a notably better execution pattern — a more efficient approach, a better tool sequence — a DERIVED skill is created as a new variant alongside the original.

An expert found a faster way to generate API clients using code generation tools. DERIVED creates a new skill variant for that approach.
CAPTURED
Triggered by: Novel pattern with no existing skill

Brand-new knowledge

When an expert successfully handles a task type that has no existing skill guidance, a CAPTURED skill is created from scratch — turning a one-time success into reusable institutional knowledge.

An expert autonomously debugged a Temporal workflow issue. CAPTURED creates a new skill for Temporal debugging patterns.
HUMAN IN THE LOOP

You decide what the platform learns

Autonomous learning without human review is risky. Every proposed skill evolution — whether a FIX, a DERIVED variant, or a new CAPTURED skill — is presented to you for approval before it takes effect.

You can accept it as-is, modify the proposed content, or dismiss it. Dismissed patterns are recorded so the assessor doesn't keep proposing the same change.

Accept
Skill is versioned and immediately active
Modify
Edit the proposed content, then accept
Dismiss
Rejected — pattern recorded to prevent recurrence

Build a platform that never stops improving

Every run is an investment. Orqista turns execution history into better guidance, so your engineering team compounds knowledge — not just output.

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