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pszemraj

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LocalLLaMA-posts

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ERROR: type should be string, got "https://preview.redd.it/8sber8twmwhg1.png?width=1767&format=png&auto=webp&s=71738b5edfdc0c7d1406276701ca7f962931788b\n\nHey r/LocalLLaMA. Nathan Sportsman here, founder of Praetorian (cybersecurity company). We just published a detailed architecture paper on the autonomous development platform we've been building internally. While the current system runs on Claude Code, a big chunk of the design is model-agnostic, and our roadmap is explicitly multi-model. Figured this crowd would have the most interesting takes on the routing and model selection pieces.\n\n**The thesis**\n\nThe bottleneck in autonomous development is not model intelligence. It's context management and architectural determinism. We kept throwing smarter models at problems that were actually caused by stuffing too much into the context window and hoping the LLM would follow instructions. Token usage explains \\~80% of performance variance in agent tasks. That means architecture matters more than model selection for most failures.\n\nBut once you solve the architecture problem, model selection becomes the *next* unlock. And that's where it gets interesting.\n\n**Heterogeneous model routing**\n\nNo single model is best at everything. Our roadmap uses a semantic routing layer (small, fast model as the router) that evaluates intent and dispatches to specialists:\n\n|Task|Model|Why|\n|:-|:-|:-|\n|Logic and reasoning|DeepSeek-R1 / V3|RL-based chain-of-thought for complex inference|\n|Document processing|DeepSeek OCR 2|10x token efficiency, visual causal flow for structural preservation|\n|UI/UX and frontend|Kimi 2.5|Native MoonViT architecture, autonomous visual debugging loops|\n|Parallel research|Kimi 2.5 Swarm|PARL-driven optimization across up to 100 agents|\n|Massive repo mapping|DeepSeek-v4 Engram|O(1) constant-time lookup, tiered KV cache for million-token context|\n\nThe point is that expensive frontier models should be reserved for tasks that actually need them. A code review agent doesn't need the same model as an architectural reasoning agent.\n\n**The architecture that makes routing possible**\n\nThe reason you can swap models per-task is that agents are stateless, ephemeral, and under 150 lines. They don't carry history. They don't accumulate context from sibling agents. Every spawn gets a clean window with only the context it needs, loaded just-in-time through gateway routers.\n\nKey patterns:\n\n* **Thin agents, fat platform.** All knowledge lives in a skill library outside the agent. Agents call a gateway (e.g., `gateway-frontend`) that detects intent and loads only the relevant patterns. No model needs to hold the full knowledge base.\n* **Deterministic hooks over prompts.** Shell scripts on lifecycle events (`PreToolUse`, `PostToolUse`, `Stop`) enforce quality gates the LLM can't override. Dirty bit tracking, test verification before exit, context compaction gates. This is the layer that makes the system model-agnostic. The enforcement logic doesn't care which model is running.\n* **Coordinators vs. executors.** Tool permissions are mutually exclusive. Agents that plan can't edit code. Agents that code can't spawn sub-agents. This separation means you can run a cheap model as the coordinator and an expensive model as the executor without worrying about role confusion.\n* **MCP tool wrappers.** Raw MCP connections were eating 71,800 tokens at startup (36% of context) across five servers. We replaced them with on-demand TypeScript wrappers. Zero tokens at startup, Zod-validated, response-filtered. This token savings matters even more when you're running smaller context windows on local models.\n\n**The DeepSeek parallel**\n\nThere's a line in the paper I think this sub will appreciate: \"Like what DeepSeek is proving to the Frontier Models, I'm not sure the expensive way is the best way anymore. The problem with capital is that it allows you to do a lot of stupid things very fast. We do not have that luxury. We must be clever instead.\"\n\nThe whole architecture is designed around the constraint that we can't just throw money at the problem. Deterministic hooks, JIT context loading, aggressive token hygiene. All of it came from needing to do more with less.\n\n**Self-annealing (roadmap)**\n\nWhen agents repeatedly fail quality gates, a meta-agent rewrites the skills and hooks that allowed the failure. The system patches its own prompt engineering. This is model-agnostic by design. The meta-agent could be any model with sufficient reasoning capability.\n\n**Escalation advisor**\n\nWhen an agent gets stuck in a loop (same failing fix three times), a hook calls an external, cheaper model (they mention Gemini/Ollama) with the transcript and asks for a one-sentence hint. That hint gets injected into the main context to break the deadlock. Great use case for a local model.\n\nFull paper: [https://www.praetorian.com/blog/deterministic-ai-orchestration-a-platform-architecture-for-autonomous-development/](https://www.praetorian.com/blog/deterministic-ai-orchestration-a-platform-architecture-for-autonomous-development/)\n\nCurious what this sub thinks about the routing matrix. What models would you slot into which roles? Anyone running similar multi-model orchestration setups locally?"