AGENTIC AI ENGINEERING · PRODUCT JUDGMENT · WORKING PROOF

I build the part of agentic AI that has to survive contact with reality.

I design, build, secure, evaluate, and ship agentic AI systems—from browser-local intelligence and governed retrieval to MCP and A2A integration, multi-agent behavior, real-time WebAssembly runtimes, and cloud-backed operational products.

The model is only one part of the system. I focus on the harder questions around it: what evidence may enter, what an agent is allowed to know and do, which state is official, when a person must approve, how failure is contained, how the result is measured, and whether the exact conclusion can be reproduced later.

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EXPERIENCE & SELECTED WORK

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END-TO-END CAPABILITY

From the product promise to the proof that it works.

I do not stop at prompt design or an impressive demo. I connect product framing, architecture, implementation, authority, evaluation, security, performance, and delivery.

  1. 01

    Product judgment

    Turn an ambitious AI idea into a useful workflow, a clear authority model, and a product people can understand.

  2. 02

    Retrieval and evidence

    Build exact, lexical, vector, graph, and hybrid retrieval with provenance, freshness, exclusions, citations, and abstention.

  3. 03

    Agents and protocols

    Design orchestration, typed tools, MCP access, A2A collaboration, state, handoffs, approvals, and long-running work.

  4. 04

    Deterministic authority

    Keep identity, permission, official state, consequential execution, verification, receipts, replay, and recovery outside model control.

  5. 05

    Local-first intelligence

    Use browser-local models, WebAssembly, Workers, durable state, and minimum-disclosure cloud escalation where the product benefits.

  6. 06

    Multi-agent behavior

    Let specialist agents coordinate, disagree, adapt, and abstain while one governed world or workflow remains authoritative.

  7. 07

    Evaluation and proof

    Measure task success, retrieval, citations, abstention, tools, state transitions, tail latency, cost, recovery, and replay.

  8. 08

    Delivery discipline

    Carry the product through security review, failure testing, target-device validation, packaging, deployment, and owner acceptance.

THE AGENTIC SYSTEMS ATLAS

One discipline, applied across very different kinds of intelligence.

The variety is the proof. Research, documents, learning, relationships, games, emulation, XR, cloud operations, and visual systems all need different experiences—but the same honesty about evidence, capability, authority, and proof.

01

GOVERNED KNOWLEDGE AND DECISION SYSTEMS

Evidence that can support a decision without quietly becoming the decision.

Governed decision system

OverLift

Evidence admission, candidate paths, approval-aware action, exact state, receipts, rewind, and replay.

Open product proof
Temporal research intelligence

Saros

What was knowable at the time, source disagreement, learned interpretation, uncertainty, and reproducible conclusions.

Open product proof
Private document intelligence

Vellucent

Page-aware evidence, Meaning Lock, proposal-only editing, immutable originals, reviewer authority, and revision proof.

Open product proof
Connected technical learning

AzureGlossary

Hybrid semantic retrieval, canonical Azure identity, command-rich practice, relationships, ranking evaluation, and citations.

Open product proof
02

HUMAN-CENTERED INTELLIGENCE

Personalization that respects uncertainty, consent, and the right not to be optimized.

Local symbolic intelligence

Veil

Exact ephemerides, private questions, longitudinal readings, visible change, bounded interpretation, and elegant uncertainty.

Open product proof
Safety-aware social discovery

Veluris

Fresh event and venue reconciliation, explicit intent, typed preferences, Q-Lens social plans, safety, uncertainty, consent, explanations, abstention, and human choice.

Open product proof
Sensitive guided workflows

TherapyLink.AI

Private intent routing, consent, narrow assistance, safety stops, uncertainty, and clear human handoff boundaries.

Open product proof
03

REAL-TIME AND MULTI-AGENT SYSTEMS

Intelligence that must obey timing, physics, machine state, and one authoritative world.

Two-agent world intelligence

Neon Drift

Zarvox and VANTA-9 coordinate, adapt, speak, and disagree while deterministic game state remains in control.

Open product proof
Speech-routed vector arcade

Vectrexia

Private speech intent, exact machine state, curated game discovery, compatibility evidence, and bounded diagnostics.

Open product proof
04

CLOUD, PLATFORM, AND VISUAL ENGINEERING

Operational and graphical systems where state, performance, and recovery are visible.

THE DISTINCTIONS THAT KEEP SYSTEMS HONEST

Good agentic engineering is knowing what must never be collapsed into one thing.

The strongest systems are designed around explicit boundaries—not vague assurances that the model will probably behave.

01

Model memoryAdmitted evidence

A remembered statement can guide retrieval; it does not become a proved fact merely because the model retained it.

02

RecommendationAuthoritative state

An agent may explain a strong next step. Exact software and accountable people decide what actually changes.

03

MCP tool accessPermission to act

A protocol can expose a capability. Identity, policy, scope, approval, and current state still determine whether it may be used.

04

A2A collaborationShared-world control

Agents can discover one another and exchange work without any participant receiving silent authority over the common system.

05

Semantic similarityTruth

Retrieval can find meaningfully related material; provenance, rights, freshness, conflict, and evidence admission decide whether it supports the answer.

06

ObservabilityEvaluation

A trace shows what happened. Evaluation decides whether the evidence, trajectory, tools, state transition, and result were good.

07

Fast pathExact baseline

WebGPU, WebGL2, SIMD, workers, and local models can accelerate work only after they prove behavioral equivalence where correctness matters.

08

AutomationConsent

The system may prepare and preview a consequential action; the affected person retains meaningful choice and revocation.

09

Cloud escalationMinimum disclosure

A difficult task may justify an external model, but only the smallest approved context should cross the chosen boundary.

GOVERNED INTELLIGENCE PROOF FABRIC

Veluris and the Learning Center now demonstrate the same new thinking discipline as Saros and Veil.

Typed plans, context-aware retrieval, temporal state, content graphs, deterministic fusion, Q-Lens candidate comparison, uncertainty, abstention, receipts, replay, and measured admission apply across domains while consent, doctrine, policy, and official state remain exact.

01

Veluris recommendationsocial authority

Fresh events, venues, preferences, safety, and mutual context can shape discovery. Disclosure, introduction, contact, and sending require current human consent.

02

Adaptive learning pathcanonical doctrine

The Glossary can infer a misconception and propose the next lesson. Reviewed sources and canonical concept identity remain authoritative.

03

Learned interpretationtemporal evidence

Saros may rank and explain evidence, but as-of records and deterministic calculations own the reproducible conclusion.

04

Human answer qualitychart state

Veil can become warmer, clearer, and more specific while exact ephemerides, accepted profiles, consent, and uncertainty remain fixed.

FROM AMBITION TO ACCEPTANCE

I build the smallest complete proof, then make every layer earn its place.

That discipline keeps a product moving while preventing retrieval, agents, acceleration, or cloud services from quietly becoming unmeasured complexity.

  1. 01

    Frame the outcome

    Define who needs help, what success means, which risks matter, and who owns the final decision.

  2. 02

    Build one complete vertical slice

    Connect the real interaction, evidence, intelligence, authority, execution, proof, and recovery path before scaling.

  3. 03

    Separate proposal from official state

    Make probabilistic reasoning useful without letting it rewrite identity, permission, facts, or consequential records.

  4. 04

    Measure the hard failures

    Test weak evidence, stale context, permission denial, duplicate action, timeouts, mismatches, cancellation, rollback, and replay.

  5. 05

    Qualify the target environment

    Validate actual browsers, devices, memory, latency tails, graphics paths, audio, network degradation, and recovery behavior.

  6. 06

    Package for a first-time owner

    Ship a clear path to run, inspect, accept, deploy, support, and reproduce the product without hidden tribal knowledge.

THE METHOD BEHIND THE WORK

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