Autonomous Code Evolution
with Quantitative Rigor

AI code tools generate suggestions. Someone still has to implement every one. AFA closes that loop: analyzes every function, generates improvements, validates through nine quantitative gates, and commits only what the math proves is worthwhile.


The Problem

The current generation of AI code tools reviews your code and generates suggestions. Every one of those suggestions still requires a human to implement, test, and commit. The result: more notifications, not fewer problems.

ChallengeAFA Solution
Suggestions with no mathematical backingNine quantitative gates with formal statistical thresholds. Your codebase improves on evidence, not opinions.
Every fix still requires manual workAutonomous commit when all gates pass. Engineering time goes to building, not triaging suggestions.
No record of what changed or whySHA-256 hash-chained decision log. Every gate evaluation is auditable, independently verifiable.
Metrics are easy to gameFour anti-gaming detectors: dead-code, edit locality, AST distance, generated code. Trust what the system commits.

How It Works

AFA runs a convergent per-function loop. It iterates until the math says no worthwhile improvement remains, then moves on. Over time, your codebase compounds quality gains without manual intervention.

Proposal → [Risk] [Profit] [Novelty] [Complexity] [Quality] [Utility] [Entropy] [Supply Chain] [KPI] ↓ COMMIT if all 9 pass + utility ratio > 1.0 SKIP if any gate fails or cost > value ↓ Audit log: SHA-256 hash chain per decision

Quick Example

# Install and analyze in 30 seconds $ pip install afa $ export GOOGLE_API_KEY="your-key" $ afa analyze src/ # Results: per-function scores + findings ┌──────────────────────┬──────┬──────┬──────┬──────┬──────┐ Function Sec Perf Main Docs Test ├──────────────────────┼──────┼──────┼──────┼──────┼──────┤ process_payment() 0.72 0.95 0.88 0.45 0.30 validate_input() 1.00 0.90 0.92 0.80 0.70 └──────────────────────┴──────┴──────┴──────┴──────┴──────┘ # Enhance with dry-run (see what AFA would change) $ afa enhance src/ --dry-run

Integration Options

Key Features

Nine Quantitative Gates
Risk, profit, novelty, complexity, quality, utility, entropy, supply chain, KPI alignment. Not heuristic scores: formal statistical thresholds derived from quantitative risk frameworks. All nine must pass.
Convergent Enhancement Loop
Per-function iteration: analyze, generate, validate, commit or skip. Terminates when no worthwhile improvement remains. Not a list of suggestions: a process that converges.
Model-Agnostic
Works with Anthropic Claude, OpenAI GPT, or Google Gemini. Switch providers via the --provider flag.
Cryptographic Audit Trail
SHA-256 hash chain per decision. Every gate evaluation gets a unique ID, timestamp, and provenance record your auditor can verify independently.
Per-Organization Pricing
Flat rate regardless of team size. Your cost stays the same whether you have 10 developers or 500.