KIMI + Agnes: A Real-World Test of Cross-Provider Agent Chain Correctover NeuralBridge, an open-source SDK for LLM pipelines, introduced 'Correctover'β€”a mechanism that verifies semantic correctness of LLM outputs before passing them to the next agent. In a real-world test, the SDK orchestrated KIMI (Moonshot) and Agnes AI in a chain, where KIMI planned architecture and Agnes wrote code, with Correctover automatically switching providers when a contract failed. The test demonstrated that traditional API gateways, which only check HTTP status codes, miss semantic failures that Correctover catches. A few days ago I had an idea: what if one LLM could orchestrate other LLMs as agents β€” not just calling them, but verifying that each agent's output was actually correct before passing it to the next? I work on NeuralBridge an open-source self-healing SDK for LLM pipelines , so I decided to build it and test it with two real providers: Most API gateways and LLM routers stop at "HTTP 200" β€” they retry or switch providers, but they never check if the output is actually correct . What everyone else does: try: result = call llm prompt return result HTTP 200 = success? 🚩 except Exception: result = call llm fallback prompt return result Still not verified This is dangerous. A failover from gpt-4o to gpt-4o-mini might silently drop 3 critical fields. A KIMI response that returns "200 OK" might still be missing key entities. Correctover is the idea that switching providers isn't enough β€” you must verify semantic equivalence after every switch. We built a simple DAG-based chain executor with three key capabilities: Contract before passing to the next node python from neuralbridge import SelfHealingEngine, ProviderConfig, Contract from neuralbridge.chain import ChainBuilder engine = SelfHealingEngine providers= engine.add provider ProviderConfig name="moonshot", base url="https://api.moonshot.cn/v1", api key="...", models= "moonshot-v1-8k", "moonshot-v1-32k" , engine.add provider ProviderConfig name="agnes", base url="https://apihub.agnes-ai.com/v1", api key="...", models= "agnes-2.0-flash" , chain = ChainBuilder engine .node name="planner", system="You are a senior architect.", prompt="Design a plan for: {task}", contract=Contract required entities= "ζžΆζž„", "樑块" , model="moonshot-v1-32k", timeout=120 .node name="coder", system="You are a Python developer.", prompt="Implement: {planner}", contract=Contract required entities= "import ", "def " , forbidden patterns= "ζˆ‘δΈθƒ½", "sorry" , model="agnes-2.0-flash", depends on= "planner" , timeout=180 .build result = chain.run task="A CSV to JSON converter with validation" KIMI plans the architecture, Agnes writes the code: | Node | Provider | Time | Contract | |---|---|---|---| | planner | moonshot-v1-32k | 17.8s | βœ… Architecture + Modules | | coder | agnes-2.0-flash | 10.8s | βœ… import + def runnable code | Total: 28.5s. The planner's design output was used as context for the coder, and the coder actually implemented the design not random boilerplate . This is where it gets interesting. In a separate test, the deep analysis node was supposed to output analysis with "δΌ˜η‚Ή" pros and "ηΌΊη‚Ή" cons : deep analysis agnes-2.0-flash β†’ Contract failed missing "δΌ˜η‚Ή"/"ηΌΊη‚Ή" ↻ Correctover triggered ↻ Automatically switched to moonshot-v1-32k β†’ βœ… Validation passed This is Correctover working in production: The first provider returned text, but it didn't satisfy the semantic contract. The engine automatically retried with a different provider, and the second attempt passed validation. In our test, Agnes AI responses took 18–233 seconds . Without proper timeouts default 8s in most SDKs , every call would fail. We had to set timeout=120 and total timeout=300 for realistic workloads. The deep analysis case above is exactly the kind of failure that traditional gateways miss: Traditional proxy: Your App β†’ Gateway β†’ KIMI β†’ 429 β†’ Gateway also 429 SDK NeuralBridge : Your App embedded β†’ KIMI β†’ 429 β†’ backoff β†’ βœ… β†’ continuous fail β†’ circuit break β†’ switch provider β†’ βœ… No extra hop, no data through third party, no infrastructure to maintain. The 2026 AI market is exploding $7.6B+ for agentic AI, 40-50% CAGR , but 88% of enterprise AI projects never reach production IDC/Lenovo . The bottleneck isn't capability β€” it's reliability. Even academia agrees: a May 2026 arXiv paper 2606.01416 https://arxiv.org/abs/2606.01416 showed that verifier-guided self-healing reduces silent failures to 0.0% , compared to 5.5%+ for retry-only approaches. We're open-sourcing the chain module as part of NeuralBridge SDK v5.x. The core engine is Apache 2.0 β€” you can use the self-healing, circuit breakers, and Correctover validation today. Try it: pip install neuralbridge python from neuralbridge import SelfHealingEngine, Contract from neuralbridge.chain import ChainBuilder NeuralBridge is an open-source Apache 2.0 self-healing SDK for LLM pipelines. Correctover β€” semantic validation after failover β€” is our core differentiator from every other LLM gateway and router.