Q&A: CactusRalph-Coder Architecture — Agent-Driven Code Generation, Tool Use & Self-Improvement Loop

[AGI-Corporation]

Overview

CactusRalph-Coder is AGI Corp's adaptive coding agent — a Ralph-lineage agent that uses the CactusRoute protocol for tool selection, integrates with NANDA for capability discovery, and feeds its performance data back into the evolution-agent epoch tracker for continuous self-improvement.

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Q1: What is the core architecture of CactusRalph-Coder?

A1: CactusRalph-Coder is structured as a 3-layer agent:

┌─────────────────────────────────────────────┐
│ Planning Layer (Sentient Foundation Model)       │
│ Decomposes task → subtask DAG                   │
├─────────────────────────────────────────────┤
│ Execution Layer (Tool Router via CactusRoute)   │
│ file_read, file_write, bash, test_runner, lint  │
├─────────────────────────────────────────────┤
│ Feedback Layer (EpochTracker + NANDA Bridge)    │
│ Scores task outcome, evolves mutation params     │
└─────────────────────────────────────────────┘

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Q2: How does CactusRoute tool selection work in the execution layer?

A2: Rather than hardcoding which tool to use for each step, CactusRalph-Coder queries the CactusRoute Capability Index at runtime:

1. The planner emits a subtask: {"type": "write_tests", "language": "python", "target": "api/routes.py"}
2. CactusRoute queries the tool registry for tools matching write_tests + python
3. Available tools (pytest generator, coverage analyzer, mock injector) are ranked by fitness_score and past_success_rate
4. Top-ranked tool is selected and invoked via MCP
5. If it fails, CactusRoute auto-retries with the next ranked tool

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Q3: What languages and frameworks does CactusRalph-Coder currently generate?

A3: Based on the mutation_params registry in the epoch tracker:

Language	Capability Level	Notes
Python	High	FastAPI, LangChain, pytest, async/await
TypeScript	High	Next.js, React, Vitest, Zod schemas
Go	Medium	REST APIs, gRPC stubs
Solidity	Medium	ERC-20/721 patterns, x402 contracts
Bash	High	CI/CD scripts, Docker entrypoints

Capability levels evolve across epochs based on test pass rates for generated code.

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Q4: How does CactusRalph-Coder feed performance back into the evolution-agent?

A4: After each coding task completes:

1. The test runner evaluates generated code: tests_passed / total_tests = raw_fitness
2. Lint score (0.0-1.0) and complexity delta are factored in: final_fitness = 0.6*test_score + 0.3*lint_score + 0.1*complexity_improvement
3. This score is logged via EpochTracker.log_performance(version_id, final_fitness)
4. High-fitness coding strategies (e.g., "always write tests before implementation") are preserved as dominant mutation_params for the next epoch

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Q5: How does CactusRalph-Coder integrate with the rest of the AGI Corp stack?

A5: CactusRalph-Coder is the code generation worker node for the entire ecosystem:

• Route.X → triggers CactusRalph-Coder via MCP when any workflow step requires code generation
• CMMC → uses it to auto-generate compliance test scripts for each of the 110 practices
• guardrails- → uses it to scaffold new plugin validators from YAML specifications
• Sapient.x → uses it to generate parcel agent TypeScript/Python stubs when new parcels are deployed
• evolution-agent → CactusRalph-Coder IS one of the agent variants being evolved across epochs