Context & problem
Independent engineering prototypes and research into operational decision support. The public grounding lab is a browser demonstration with synthetic data.
Problem
An apparently confident AI answer can be unsupported, stale, or unrelated to the current system state. Operational recommendations need traceable evidence and explicit authority boundaries.
My contribution
I develop technical writing and browser demonstrations exploring retrieval, advisory troubleshooting, MCP-style tool access, and human-reviewed agent workflows.
Architecture & decisions
The reference path collects an operational question, retrieves context, separates observations from hypotheses, and presents recommendations for human review. Tool execution would require policy checks and a recorded approval; the public lab does not execute infrastructure changes.
Reference sequence: Question & context → Retrieve evidence → Draft recommendation → Human review.
Technology
HTML, CSS, and JavaScript power the browser lab. RAG, embeddings, MCP, and agent orchestration are illustrated through preset response paths; there are no live model or enterprise integrations.
Key decisions
Display evidence and uncertainty with an answer. Separate retrieval similarity from diagnosis confidence. Keep production execution outside advisory tools unless independently authorized and auditable.
Delivery & validation
The browser interface compares fixed response patterns and demonstrates exact-cache hits, misses, scope separation, source-version changes, and expiration. Live retrieval, model evaluation, and governed operational integrations remain planned.
Security & operations
Treat retrieved documents and model suggestions as untrusted input. Bound tool permissions, preserve the distinction between recommendation and approval, and keep production credentials outside demonstrations. The current browser lab has no operational execution access.
Validation
The browser demonstration can be checked for scenario selection, displayed sources and explicit simulation labels. A future live system needs retrieval-quality checks, missing and conflicting evidence cases, prompt-injection tests, permission-denial tests and recorded human approval before tools run.
Trade-offs
Retrieval does not guarantee correctness. Sample scenarios can explain a pattern but cannot prove effectiveness on real incidents. Agent coordination increases failure modes and needs evaluation before operational use.
Outcomes & status
The lab lets readers compare response structures, visible sources, and uncertainty in synthetic scenarios. It has not been evaluated for live model accuracy or incident reduction.
Scope & status
Browser demonstration is implemented. Live enterprise RAG/MCP and production agents remain planned; TVR mechanisms are research proposals.