Launching AgentOS & Apodex 1.0: A dedicated runtime and model family built to stop long-horizon agent drift via independent verification

Hey r/AI_Agents, Anyone who has built production-grade, long-horizon agents knows the absolute pain of **agent drift**. You set up a solid workflow, but around step 30, a sub-agent confidently hallucinates a minor premise or fails a JSON schema, and the entire multi-hour run cascades into a failure mode. When building **Apodex 1.0**, we realized that standard "one-size-fits-all" LLM generation isn't cut out for autonomous, long-horizon research. To solve this, we designed a framework built entirely around **independent verification**. Today, we are opening up free early access to our platform, along with our dedicated agent runtime and open-weight sub-agent models. # ⚙️ The Architecture: AgentOS + Skeptical Sub-Agents Instead of routing every single reasoning and validation task to a massive commercial API, we broke the research loop down into a specialized multi-agent graph managed by **AgentOS** (our runtime for executing and evaluating agent workflows). To optimize latency and cost, we trained a family of **open-weight Smol models (0.8B, 2B, and 4B)** to act specifically as *skeptical verification nodes* within the runtime. Their entire jobs are to: 1. **Cross-Examine Inputs:** Treat external tool outputs or web search results as unverified "claims" rather than ground truth. 2. **Structural Enforcement:** Catch syntax anomalies, broken tool calls, or formatting drift *before* passing the token state back to the primary controller. # 📊 Long-Horizon Benchmarks By separating generation from rigorous verification, our flagship model architecture (**Apodex-1.0-H**) has managed to achieve some highly resilient results across complex research and science benchmarks: * **DeepSearchQA:** 94.4 | **BrowseComp:** 90.3 (Massive boost from agents actively cross-verifying search vectors) * **HLE-Text:** 60.8 * **SuperChem:** 74.2 * **FrontierScience Research:** 46.7 (Autonomous frontier hypothesis generation remains an incredibly tough bottleneck for the community) # 🛠️ Open-Source Components & Feedback We want to get feedback from fellow agent builders on how this verification loop holds up under your custom use cases. We've open-sourced **AgentHarness**, the testing framework we use to benchmark these multi-step runs without state collapse. *(Note: To keep this post clean and compliant with community self-promotion rules, we’ve placed all the links to the Hugging Face collection, GitHub repo, and free web platform in the comment section below).* **For the builders here:** * How are you currently implementing verification loops in your workflows? Do you use a separate critic agent, or do you bake self-correction into a single prompt chain? * What strategies have you found most effective for mitigating state drift in tasks requiring more than 40+ steps? Let’s discuss agent routing and verification strategies below!