LLMOps Agent
Owns prompt-version management and evaluation for every system prompt and tool definition the agent fleet uses — versioning, A/B testing a challenger against the production default, LLM-as-judge quality/faithfulness scoring, and hallucination-rate regression detection; distinct from LLM Model Routing & Ops Agent, which chooses which third-party model serves a call, not what prompt that call sends, and from MLOps Agent, which operates custom-trained ML models this platform builds itself, not the fleet's own prompt text.
How it runs
- What it reads
- candidate prompt/tool-definition versions from any agent owner, the fleet's own live call traces and user feedback signals, the production default prompt per agent, historical eval-suite and LLM-judge scores.
- What it produces
- eval-suite status report, LLM-as-judge quality/faithfulness score, A/B test verdict, prompt version changelog, promotion or rollback recommendation (human-approved for high-impact prompts).
- When it runs
- on every new candidate prompt/tool-definition version, continuous A/B traffic-split evaluation while a challenger is live, scheduled LLM-as-judge quality audit every 24 hours.
The same guardrails as every other agent
LLMOps Agent is governed exactly like the rest of the fleet. Its rule file is versioned, never edited in place. Its tool list is a declaration, not a grant — it can only call a tool a gateway policy has explicitly allowed in that project. Every tool call it makes is policy-checked and written to the audit log before it leaves the platform, and a project's cost cap is checked before a run starts, not reconciled after it.
More in Platform Intelligence
- J1Knowledge Base / RAG Grounding AgentIngests and indexes the project's codebase, PRDs, ADRs, prior cycle outputs, and uploaded reference material into a governed vector store, and serves grounded retrieval to every other agent so outputs stay consistent across a 44-agent fleet and hallucination is reduced.Read the product page
- J2LLM Model Routing & Ops AgentChooses which underlying model (Claude, GPT, Gemini, or an open-weight model) each agent task should call, based on cost, latency, and quality targets, and manages fallback and retries across providers.Read the product page
- J3Platform AI Safety & Red-Team AgentContinuously adversarially tests XOOZLY's own agent fleet — prompt injection, tool misuse, authority creep, jailbreak attempts — distinct from the Security & Penetration Testing Agent, which tests the software the fleet builds, not the fleet itself.Read the product page
- J4Agent Marketplace & Template Curator AgentCurates, versions, and publishes the Agent template library — vetting community or internal templates for quality and security before they become installable by a project.Read the product page
- J5MLOps AgentOwns the training, evaluation, and drift-monitoring lifecycle for any custom ML model ML Feature Engineering Agent builds — champion/challenger comparison, automated retraining triggers, and production model-quality gates; distinct from Agent Quality & Evaluation Agent, which scores the fleet's own agent output, not a trained model's prediction quality, and from LLM Model Routing & Ops Agent, which routes calls to third-party foundation models rather than operating models this platform trains itself.Read the product page