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Document Intelligence
Grounded retrieval, structured document workflows, controlled generation, and enterprise knowledge access.
MCP toolsRAG pipelinesAzure FunctionsManaged Identity
Enterprise AI Systems Studio
Agentic AI · Forward Deployed Engineering
I build AI systems.Not just demos.
I architect and build enterprise agentic systems that route work across specialist agents, expose focused capabilities through MCP, ground answers in governed data, and choose the execution pattern that fits the problem.
The Fleet
Specialist agents for real enterprise work.
A purpose-built fleet where each specialist gets the context, tools, permissions, and boundaries needed for its job.
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Vision Intelligence
Multimodal reasoning with screening and safety gates before model responses are returned.
Vision modelsSafety guardrailsPHI-aware screeningManaged Identity
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Enterprise Knowledge
Read-focused search and retrieval across collaborative knowledge and work-management systems.
MCP connectorsConfluence + JiraSemantic retrievalRead-only by design
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Learning Intelligence
User-scoped learning experiences where results follow the signed-in user's delegated permissions.
Per-user OAuthLMS integrationPersonalized resultsRead-only by design
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Domain Intelligence
Specialist agents for product and operational scenarios backed by governed structured data and test cases.
Scenario storesCosmos DBRBAC accessRead-only by design
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Workflow Intelligence
Async job pipelines and automation across queues, storage, and cloud services for longer-running AI workflows.
Async pipelinesStorage queuesJob orchestrationManaged Identity
Selected Work
Different problems. Different AI patterns.
A sanitized view of systems I have designed and built. The focus here is the engineering pattern and use case — not employer code, internal names, customer data, endpoints, or proprietary implementation details.
BUILD 01
Enterprise Document Intelligence
Challenge: make a large, changing body of product knowledge usable by an agent without turning retrieval into an ungoverned black box.
Build: a Python-based specialist tool service with a broad read-focused surface for search, retrieval and discovery, plus a narrowly controlled generation path. Content indexing is maintained incrementally rather than rebuilt blindly.
Execution pattern
USER → ORCHESTRATOR → DOCUMENT SPECIALIST → MCP TOOLS → GOVERNED KNOWLEDGE
Engineering choice: predominantly read-only tools, scoped writes, short-lived access, and identity-based cloud access.
BUILD 02
Multimodal Vision Intelligence
Challenge: let an enterprise agent reason over images while keeping image handling ephemeral and applying content screening before returning model output.
Build: a read-only vision specialist that sends image content directly to a multimodal model, applies screening logic, and does not persist image bytes to application storage.
Execution pattern
IMAGE → VISION SPECIALIST → SCREENING → MULTIMODAL MODEL → RESPONSE
Engineering choice: content-based safety boundary with no image persistence in the tool layer.
BUILD 03
Enterprise Knowledge Retrieval
Challenge: ground answers in collaborative knowledge and work-management platforms without giving an AI system unnecessary mutation privileges.
Build: a focused integration layer for knowledge search, page retrieval and work-item search, intentionally constrained to read-oriented operations and known information domains.
Execution pattern
QUESTION → KNOWLEDGE SPECIALIST → SEARCH / RETRIEVE → GROUNDED ANSWER
Engineering choice: retrieval-first capability with explicit boundaries rather than broad platform access.
BUILD 04
Identity-Aware Learning Intelligence
Challenge: enterprise learning results should follow the real signed-in user rather than a shared service identity.
Build: a learning specialist using delegated OAuth so retrieval is scoped to each user's actual permissions and learning context.
Execution pattern
SIGNED-IN USER → SPECIALIST → DELEGATED OAUTH → USER-SCOPED RESULTS
Engineering choice: identity travels with the request instead of collapsing every user into one shared credential.
BUILD 05
Domain Scenario Intelligence
Challenge: expose structured domain scenarios to an agent without relying on free-form model memory or production-sensitive data.
Build: a read-only specialist backed by a structured scenario store, using cloud identity and RBAC to retrieve deterministic test-case knowledge.
Execution pattern
DOMAIN QUESTION → SPECIALIST → MCP → SCENARIO STORE → EXPLAINED RESULT
Engineering choice: structured retrieval for domain logic; scenario data stays separate from model reasoning.
BUILD 06
Asynchronous AI Workflow Pipeline
Challenge: some AI work is too long-running and multi-stage for a single synchronous chat/tool call.
Build: an asynchronous pipeline spanning extraction, validation, synthesis and job-status retrieval, coordinated through cloud storage and queue-backed execution.
Execution pattern
REQUEST → JOB → QUEUE → MULTI-STAGE AI PROCESSING → STATUS / RESULT
Engineering choice: decouple long-running work from the conversational request and make progress queryable.
BUILD 07
Standalone AI Training Assistant
Challenge: not every enterprise AI experience belongs inside an MCP-backed conversational orchestrator.
Build: a standalone full-stack agent experience with a React frontend, Python API backend, repository tooling, managed secrets and model integration.
Execution pattern
REACT UI → API BACKEND → REPO TOOLS / MODEL → GUIDED TRAINING EXPERIENCE
Engineering choice: use a dedicated application surface when workflow and identity needs differ from the agent mesh.
My Enterprise Agent Mesh
A fabric, not a chatbot.
The experience stays coherent while specialist agents, tools, models, identity, and enterprise systems evolve independently.
Design principle: keep specialists focused, permissions narrow, tool execution observable, and the integration layer independently replaceable from the conversational experience.
Experience
User / Team InterfaceConversational UX
Orchestration
Intent + RoutingTask DecompositionPolicy Boundaries
Specialists
DocumentVisionKnowledgeLearningDomainWorkflow
Tool Fabric
MCPPython ServicesREST APIsCloud Functions
Enterprise Data
Knowledge StoresOperational SystemsCloud DataQueues
Trust Plane
Managed IdentityOAuthRBACSafety GatesOpenTelemetry
R&D · architecture in progress · not live
Building memory into the mesh.
A separate architecture track exploring how a multi-agent system can retain verified corrections, personalize only where governance allows it, and learn from aggregate usage without treating every form of memory as the same thing.
Phase 1 · Recommended first
Shared CorrectionsCurated, verified facts shared across specialists so known corrections can be checked before a similar answer is composed again.Phase 2
User ContextScoped preferences and recurring context only where identity, privacy, classification, and retention policies explicitly allow it.Phase 3
Usage IntelligenceAggregated signals across tool calls, topics, fallbacks, failures, and outcomes to inform the next iteration of the system.Engineering Surface
Hands-on across the AI stack.
Architecture is only useful if it ships. My work spans agent design, tool contracts, code-assisted development, cloud execution, identity, data, observability, and enterprise integration.
Daily Builder Tool
Claude Code
Used heavily for implementation, refactoring, debugging, repo-level reasoning, and accelerating AI engineering workflows.
Agent Runtime
Copilot Studio
Orchestration, child-agent patterns, enterprise integrations, and conversational system design.
Tool Protocol
MCP
Reusable tool contracts for specialist agents and independently deployable enterprise capabilities.
Language
Python
Serverless MCP services, tool logic, validation, and integration workflows.
Cloud
Azure Functions
Serverless deployment surface for independently scalable AI tools and workflow services.
Models
Azure OpenAI
Generative and multimodal model integration inside enterprise-governed systems.
Identity
Managed Identity + OAuth
Keyless and delegated access patterns with RBAC and least-privilege boundaries.
Observability
OpenTelemetry
Prompt, tool, failure, and usage telemetry through Application Insights and downstream analysis.
Data
Cosmos DB + Blob
Scenario stores, knowledge data, queues, and serverless persistence patterns.
Integration
REST APIs
Connecting agents to enterprise systems outside the MCP boundary when needed.
Frontend
React + JavaScript
Custom agent experiences and standalone AI application surfaces.
Platform
Docker + Kubernetes
Containerized application patterns, deployment, scaling, and cloud-native operations.
Build AI that knows when to reason, when to retrieve, when to use a tool — and when not to guess.
Specialized intelligence. Governed access. Observable execution. Systems that solve real enterprise work.