Rocket Agent

The problem LLM agents fail in customer service for a reason that has nothing to do with model quality: local context. The policies, eligibility rules, and edge cases that define how one lender actually operates are unique to that lender, constantly changing, and scattered across systems. RAG addresses this by injecting snippets at inference time but it reads and forgets. Nothing about a conversation makes the next conversation better. The gap is sharpest in what we'd call complex consumer service: not "where's my order," but "change my payment date." That requires customer context, product state, an action proposal, validation against real eligibility rules, and a write to a system of record. Get it wrong and you've moved money incorrectly. Most agents avoid this by staying read-only. What we built An agent that pursues a goal across engagements. A case carries a plan, an outcome, and its own follow-up schedule — the agent decides when to re-engage and records why. Its context lives in MongoDB Atlas in three slices: authored policy, per-customer episodic history, and lessons the agent derived from its own outcomes. The loop is the point. When a case closes — won or lost — every lesson that influenced it has its win/loss record updated, and a reflection pass reads the transcript and writes new lessons back. Retrieval then prefers what actually works and drops what doesn't. In our demo, a losing case moves a lesson from 9/11 to 9/12 and it ranks lower on the next call. Write actions are gated by deterministic code, not the prompt. The model proposes; validation decides. A hallucinated action fails eligibility instead of moving money. MongoDB Retrieval is one aggregation: autoEmbed generates embeddings in-database, $rankFusion fuses vector search with BM25, and $rerank applies a cross-encoder — with zero outbound model calls. Atlas also holds case state, turn-level checkpoints for crash recovery, and the outcome log the learning loop runs on. Voice is ElevenLabs Conversational AI, with our orchestrator as its custom LLM — so phone and chat run identical logic.