LLM workflow design
Turn a multi-step task into reviewable work with explicit context, outputs and decision boundaries.
Applied LLMs · Agent systems · Governed delivery
LLM & Agent Systems
Multi-step AI work needs more than fluent output: reliable context, reviewable execution and decisions people can own. This portfolio shows how I connect those concerns in a working prototype.
My contribution and scope
I configured and combined existing LLM assistants, coding agents and local inference tools with a Git-backed context layer, evidence classification and a human-reviewed delivery workflow. The contribution is the operating design, integration and evaluation around those tools. This work does not claim authorship of the foundation models or agent products.
Documented prototype work includes curated AI-readable context, a locally smoke-tested read-only MCP interface, and recovery testing. These demonstrate bounded engineering work; they do not establish enterprise adoption, general autonomy or production-scale performance.
Agent orchestration
Coordinate capabilities and handoffs while preserving human authority over material decisions.
Evaluation and evidence
Distinguish fluent output from a supported result. Keep validation, source limitations and uncertainty visible.
Reliability and recovery
Test durable workflow state and recovery under bounded interruptions, keeping mechanical results separate from business value.
Architecture and trade-offs
- 01
Frame the problem and context
- 02
Coordinate bounded execution
- 03
Evaluate outputs and evidence
- 04
Review results and decide
Human review at material decisions
Review adds time and limits autonomy, but preserves accountability when a plausible answer is not sufficient evidence to act.
Bounded context access
A curated read-only interface reduces what the system can access or change. Its narrower scope trades convenience for a more inspectable trust boundary.
Selected evidence and limitations
Workflow recovery
Bounded interruption tests demonstrated recovery of workflow state across crashes and sessions. This supports the tested mechanism; continuous production reliability and human productivity gains remain unproven.
Governance discovery
Local discovery checks passed in 5 of 5 accepted repositories. Two stale or conflicting control scenarios failed closed. These are control-plane test results, not evidence of general agent autonomy.