Dewu's 'TuiChaCha' Agent Turns Recommendation-System Debugging Into a Self-Learning Assembly Line
Most AIOps agents are either brittle scripts or unbounded LLM explorers. Dewu's architecture shows how to fuse both into a single system that gets faster and more reliable over time—without retraining a model—by converting successful ad-hoc reasoning into verified, deterministic workflows.
Dewu's recommendation team built a hybrid agent, 'TuiChaCha,' that splits diagnostic work between a deterministic Highway mode for known issues and an autonomous ATV mode for novel failures. The Highway executes pre-scripted Stories—ordered sequences of atomic API Skills—while the ATV uses a ReAct loop constrained by a six-iteration limit, a tool whitelist, and a confidence threshold to explore root causes. A scheduler routes each query to the right mode and falls back when a script stalls.
A separate evolution layer closes the loop. After ATV solves a long-tail problem, the system prunes the reasoning trace to its essential steps, generalizes hard-coded IDs into business variables, and generates a new Story. That Story must pass a dry-run replay against the past week's cases with over 90% accuracy before it is promoted to the Highway. The knowledge base combines OpenViking for memory and retrieval with Graphify to build a token-efficient knowledge graph from code and documents, layered by detail to avoid context bloat.
A real-world example shows the system diagnosing a duplicate phone-case recommendation: it traced the item through the TIDE recall channel, identified the user's recent browsing signal as the trigger, and concluded it was normal behavior, not a bug, while also surfacing the relevant internal SOP for the operator.
The architecture inverts the usual agent-evolution narrative: instead of hoping a model gets smarter, it systematically converts successful ad-hoc reasoning into deterministic scripts, making the system more predictable and cheaper to run over time.
The 90% dry-run accuracy gate before a new Story enters the Highway is a practical quality-control mechanism that most self-improving agent designs omit, and it prevents a bad generalization from silently degrading the fast path.
Coupling a ReAct agent with a hard iteration cap and a tool whitelist is a low-cost way to get exploratory power without the unbounded token burn and hallucination risk that make pure LLM agents impractical for production SLAs.
The knowledge-base design—OpenViking for retrieval and Graphify for structured parsing—addresses a common failure mode where agents don't know when to consult documentation, though the team acknowledges the trigger strategy still needs work.