I architect and ship production-grade autonomous agents for enterprise operations across finance, legal, procurement, healthcare, and government — systems that reason openly, degrade gracefully, and survive contact with the real world.
I design and ship production-grade autonomous agents — and the open infrastructure beneath them. My work runs live across finance, accounting, payroll, lending, procurement, legal, healthcare, and D2C operations.
The throughline is a conviction the industry is only now catching up to: agents should reason in the open, fail where you can see it, and survive contact with the real world. That philosophy is codified in OpenAgent, the open reference pipeline I author under OpenGraph.tech.
Pilot AI looks magical in a demo and collapses in production. The failures are predictable — and each one has an engineering answer, not a prompt-tweak.
I extract a typed goal with constraints, success criteria, and alternative interpretations before a single token of work is generated.
An epistemic-humility layer audits each request along fixed dimensions and flags the known unknowns by severity — a decision gate, not a gut feel.
The clarifier searches the web first, auto-resolves what it can, and spends user attention only on what's genuinely unknowable. One question, not seven.
Intent becomes a dependency-aware DAG of numbered, independently verifiable steps — auditable and editable before anything runs.
Steps run in dependency order with streamed output and a final trace-to-goal pass that maps the deliverable back to the original intent.
Systems degrade, never die: missing keys fall back to in-memory and local paths. Graceful degradation is a feature, not an error.
I author and ship OpenAgent under OpenGraph.tech — the open standard for business agents that reason openly, not opaquely. Five typed specialists, one streaming pipeline, ~4,000 lines of code you can actually audit.
Live revenue products, enterprise demos, and open research — each one a different slice of the same discipline: agents that hold up under real operational load.
A production AI platform that turns tax planning and financial decisions into guided, agent-driven workflows — reasoning over real financial context in real time.
Visit product ↗An AI system for the tendering and procurement lifecycle — surfacing, parsing, and acting on live tender opportunities at the speed enterprise bid teams actually need.
Visit product ↗An end-to-end autonomous operations layer for direct-to-consumer brands — agents that run the day-to-day of the business, coordinating tasks across the stack so the team can stop firefighting and start scaling.
Watch it run ↗An autonomous accounting agent that books, categorizes, and reconciles with audit-ready, defensible output.
Watch demo ↗A payroll agent that handles the full run — calculation, compliance, and edge cases — without the monthly scramble.
Watch demo ↗A vertical agent for the trades — taking inbound jobs from intent to booked, dispatched, and followed-up, fully autonomously.
Watch demo ↗An agent that performs structured credit analysis — assembling, reasoning, and defending a lending view.
Watch demo ↗Autonomous matching and reconciliation that collapses a manual finance-ops grind into a verifiable pass.
Watch demo ↗A voice-driven agent that takes spoken intent straight into structured procurement actions.
Watch demo ↗Open research applying large behavioural modeling to predict performance outcomes — the deeper ML foundation under the applied work.
View repository ↗The five-stage reference pipeline behind everything above: Intent → Ambiguity → Clarifier → Planner → Executor. Typed contracts at every boundary, graceful degradation by design, ~4k readable lines. The thing I run, ship against, and learn from.
Read the codebase ↗From the model boundary to the boardroom outcome — I own the whole reasoning chain end to end.
Multi-stage agents that plan, act, and verify — built to ship, not to demo.
Spoken-intent pipelines that turn natural language into structured enterprise actions.
Model-agnostic integration into real operational systems, security, and workflows.
Retrieval that fans out in parallel and grounds reasoning in your real knowledge base.
Reconciliation, credit, tax, and lending agents with auditable, defensible output.
High-stakes domains where epistemic humility and traceability are non-negotiable.
Voice-to-procurement and tendering agents across the full bid lifecycle.
Designing the typed, inspectable systems that make the rest of it possible.
The frameworks I publish are the same ones I ship. No theory I haven't run in anger.
Bring the messy version of the problem. In one working session we'll figure out whether an agent is the right answer — and exactly which of the five stages your last attempt died in.