I tested my GenOS for LLM agents. It fixed prompt bloat and replaced multi-agent swarm latency.
artificial
I tested my GenOS for LLM agents. It fixed prompt bloat and replaced multi-agent swarm latency.
I ran an empirical test on GenOS, an environment where LLM agents are driven by a versioned YAML "genome" rather than massive prompts. By mutating traits (e.g., risk_tolerance) and breeding specialized agents together, I achieved emergent TDD...
artificial

I tested my GenOS for LLM agents. It fixed prompt bloat and replaced multi-agent swarm latency.

I ran an empirical test on GenOS, an environment where LLM agents are driven by a versioned YAML "genome" rather than massive prompts. By mutating traits (e.g., risk_tolerance) and breeding specialized agents together, I achieved emergent TDD, bypassed RAG context limits, and entirely avoided multi-agent "ping-pong" loops.

I set up a real test environment (Windows/PowerShell, Node v24, ESLint, Rust CLI) with a severely flawed PaymentProcessor.ts file. It had 38 lint errors and a silent security hole (adding USD to EUR accounts without conversion).

Here is what I found when testing different AI paradigms against it:

1. The Prompting Baseline (Failed)

  • Simple Agent: Given a basic "refactor this" prompt (~15 tokens). It cleaned the style but left 3 lint errors and preserved the silent security hole.
  • Expert Agent (Heavy Prompt/RAG): I injected ~600 tokens of strict ESLint rules and PCI-DSS standards.
    • Result: It fixed the currency bug, but still failed the linting constraints on the first try. It took 3 iterations to reach 0 errors. Massive token overhead for a mediocre first-pass result.

2. Emergent TDD via "Genome" Mutation

Instead of huge prompts, I used the GenOS Rust CLI to mutate an agent's YAML genome.

  • I set risk_tolerance ≈ 0.10 and verification_threshold = 0.80.
  • Result: The agent refused to touch production code directly. It autonomously wrote 4 scope tests first (emergent TDD), which immediately caught the EUR/USD security hole.
  • Next, instead of injecting ESLint rules, I mutated its syntax_strictness to 0.9.
  • Result: 0 lint errors and 5/5 passing tests. Zero extra tokens added to the prompt. The trait is persisted in the agent's versioned YAML (v0.1.2) for future use.

3. "Breeding" Replaces Multi-Agent Swarms

Usually, if you need secure AND highly performant code, you use a multi-agent framework (a coder, a security auditor, a perf engineer) that wastes time and tokens debating each other.

  • I took two parent agent genomes (SecurityAuditor and PerfEngineer) and used the CLI to breed them into a single Child_Crypto.yaml.
  • Result: In a single pass, the child agent wrote an AES-256-GCM encryption engine that passed all security linting and hit a throughput of 21 ops/ms on a 5000-batch test. No swarm ping-pong, no endless LLM loops.

Has anyone else experimented with persistent parameter files or "genetic" traits for local agents instead of relying purely on RAG and system prompts?

submitted by /u/MonokoEloba
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