L1 · What should agents do?
Venkat AI Platform
Multi-agent orchestration OS
Tradeoff: LangGraph stateful graphs over linear chains — enterprise workflows need checkpoints and HITL.
Metrics loading or API waking…
5-spine review path (4 live + ADR hub) · 18 repos
AI architect portfolio — production systems you can click, inspect, and fork.
Applied AI engineering portfolio spanning agent governance, multi-agent orchestration, enterprise RAG, and AgentOps. Every project ships with its architecture diagram, a live platform, and the open-source repository on one card.
Start Here (5 min)
Technical review path for engineering panels
AegisAI → VAP → Enterprise RAG → Content Factory → ADRs (Demo vs Strict labeled). Full checklist →
Eagle-eye org stack
Ten architecture questions — each with a live demo and production metrics
Click into any layer. Metrics pull from public /ops/metrics on Render — architecture decisions backed by running systems, not slide decks.
L2 · What are agents allowed to do?
AegisAI
Gateway, policy, HITL, audit
Tradeoff: Control plane over agent builder — enterprises need governance across fleets, not another chat UI.
Metrics loading or API waking…
L3 · How do we route and quota model calls?
aegis-llm-gateway
LLM gateway plane — OpenAI-shaped proxy, stub/BYOK
Demo · metrics on hosted API when deployed
L4 · How do we similarity-cache completions?
aegis-semantic-cache
Semantic cache plane — tenant-scoped embed + lookup
Demo · metrics on hosted API when deployed
L5 · What knowledge can they use?
Enterprise RAG Platform
Access-aware governed RAG
Tradeoff: Authorization before ranking — vector DB is implementation; access control is architecture.
Metrics loading or API waking…
L6 · How do we adapt models to domain format?
DomainForge
RAG + QLoRA triage pipeline
Tradeoff: RAG for facts, LoRA for schema — never memorize policies in weights.
Metrics loading or API waking…
L7 · How do we run real-time voice triage?
VoiceForge
ASR → LLM → TTS with latency budgets
Tradeoff: Browser ASR on Render; server Whisper opt-in on GPU hosts.
Metrics loading or API waking…
L8 · How do we operate agent fleets?
AegisLoop AgentOps
Missions, traces, eval gates
Tradeoff: Missions over monolithic prompts — specialist handoffs with observable traces.
Metrics loading or API waking…
L9 · What do they produce?
AI Content Factory
Governed content publish pipeline
Tradeoff: AegisAI gateway blocks publish until policy allows — side effects never bypass governance.
Metrics loading or API waking…
L10 · How do agents improve?
LoopForge
Loop harness · RAG tuning · memory
Tradeoff: Harness + eval loops over bigger prompts — ODAEU tunes RAG on failure.
Metrics loading or API waking…
L11 · How do we serve LLMs at scale?
vLLM Architecture Lab
PagedAttention · continuous batching · KV budget
Tradeoff: Educational simulator before GPU spend — teams learn inference architecture without H100 budget.
Demo · metrics on hosted API when deployed
L12 · How do we answer multimodal asks with the right models?
OmniForge
Multi-agent multi-LLM routing lab
Tradeoff: Self-contained monorepo — no sibling runtime deps; routing is architecture, not a dropdown.
Demo · metrics on hosted API when deployed
L13 · How do we scan overnight signal?
Sentinel Brief
Governed intelligence brief · email
Tradeoff: Autonomous inner loop, governed outer loop — only email send goes through gateway.
Metrics loading or API waking…
Agent FinOps
Usage metering + budget breach detection — gates LLM dispatch across the fleet.
Metrics loading…
Governed AI Reference Stack
13 architecture questions. 12 production answers.
Each layer answers a question enterprise agent programs must resolve before production.
2
What are agents allowed to do?
Gateway, policy, HITL, audit
aegisai-enterprise-agent-platform
3
How do we route and quota model calls?
LLM gateway plane — OpenAI-shaped proxy, stub/BYOK
aegis-llm-gateway
4
How do we similarity-cache completions?
Semantic cache plane — tenant-scoped embed + lookup
aegis-semantic-cache
6
How do we adapt models to domain format?
RAG + QLoRA triage pipeline
domainforge-rag-peft
7
How do we run real-time voice triage?
ASR → LLM → TTS with latency budgets
voiceforge-assistant
8
How do we operate agent fleets?
Missions, traces, eval gates
aegisloop-agentops-workbench
10
How do agents improve?
Loop harness · RAG tuning · memory
loop-engine-agent-platform
11
How do we serve LLMs at scale?
PagedAttention · continuous batching · KV budget
vllm-architecture-lab
12
How do we answer multimodal asks with the right models?
Multi-agent multi-LLM routing lab
omniforge
Live Platforms
Click in. Read traces. Evaluate architecture decisions.
Each card = decision + signal + limitation + platform + repo
01Agent governanceGatewayHITLPolicy
AegisAI
Runtime control plane for agent fleets — gateway, policy, HITL, and audit.
Monitor → Govern → Remediate — a runtime control plane for tool authorization, policy violations, HITL approvals, signed audit, and agent registry lifecycle.
Decision
Control plane over agent builder — enterprises need governance across fleets, not another chat UI.
Signal
Website deploy tools forced through approval_required policy on live platform.
Limitation
Registry defaults to in-memory on free tier; Postgres path documented for production.
What shipped: AegisAI: gateway-first governance control plane →
- AI Gateway SDK with OPA policy evaluation
- Human-in-the-loop for high-risk tool calls
- Agent registry with in-memory persistence (Postgres planned)
- Governed orchestrators: content pipeline, stock research
02Multi-agent OSLangGraph16 IntentsRAG Lab
Venkat AI Platform
Multi-agent OS with three orchestrators, seven RAG strategies, and gateway-wrapped delivery.
Three LangGraph orchestrators, seven RAG strategies, loop patterns (ReAct · Reflection · Plan-Execute), and gateway-wrapped delivery to Slack, Telegram, and WhatsApp.
Decision
LangGraph stateful graphs over linear chains — enterprise workflows need checkpoints and HITL.
Signal
16 routed intents · 7 retrieval strategies · AegisAI gateway on notify channels.
Limitation
Vector store optional on free tier; Enterprise RAG adapter bridges to governed RAG platform.
What shipped: Venkat AI Platform: three LangGraph orchestrators live →
- Platform · Deep Research · Architecture Review orchestrators
- Enterprise RAG adapter as 7th retrieval strategy
- AegisAI gateway on notify channels
- Specialist agents: Web, Knowledge, Critic, Planner
03Knowledge layerHybrid RAGAccess ControlGraph Expansion
Enterprise RAG Platform
Access-aware hybrid RAG with ingest, citations, and optional HITL gates.
Authorization before ranking, hybrid retrieval, cross-encoder rerank, decline-to-answer, citation traceability, AegisAI HITL bridge, and Langfuse trace export.
Decision
Authorization before ranking — vector DB is implementation; access control is architecture.
Signal
golden-eval-registry CI · adversarial suite · JWT Principal when PRODUCTION_STRICT=1.
Limitation
Default live demo is Demo mode (body Principal) with sticky banner; Strict/JWT is the Principal review path (ADR-0006). Free-tier corpus re-ingests after cold start.
What shipped: Golden eval registry gates real CI builds →
- Access-aware filtering before semantic ranking
- Hybrid lexical + semantic retrieval with cross-encoder rerank
- Decline-to-answer when retrieval confidence is low
- JWT-verified Principal under PRODUCTION_STRICT · adversarial golden suite
04Knowledge + MLOpsQLoRARAGEval Harness
DomainForge
Governed support triage — RAG corpus + QLoRA JSON envelope + S0–S2 eval compare.
End-to-end support triage: capstone SOP RAG, Bitext QLoRA + DPO, S0→S4 eval ladder, Ollama bench UI, adapter registry — RAG holds facts, PEFT holds JSON discipline.
Decision
RAG for facts, LoRA for schema — never memorize policies in weights.
Signal
S0→S4 compare · DPO preference win-rate · `/bench` Ollama metrics UI · `VLLM_BASE_URL` Path B.
Limitation
Mistral QLoRA requires GPU; Render API uses mock/template baseline unless Ollama/vLLM Lab wired. Path B is educational (not CUDA multi-LoRA).
What shipped: DomainForge: RAG + QLoRA support triage pipeline →
- 13 SOP docs — hybrid governed retrieval
- Bitext → QLoRA SFT + DPO alignment (S3/S4)
- S0→S4 compare harness + preference pairs
- Ollama bench UI · GPU pipeline docs · educational vLLM Path B (ADR-022)
05Voice / MultimodalASRTTSWebSocket
VoiceForge
Closes portfolio Pillar 5 — measurable ASR / LLM / TTS latency with honest free-tier defaults.
Browser-first ASR on Render free tier, pluggable LLM (mock / Ollama / DomainForge), edge-tts with browser fallback, per-phase latency waterfall, and graceful degradation to text.
Decision
Browser ASR on Render; server Whisper opt-in on GPU hosts.
Signal
9-test CI · P50/P95 latency waterfall UI · phase budgets (ASR/LLM/TTS).
Limitation
Browser ASR quality varies; mock LLM default on Render until Ollama host wired.
- Browser Web Speech API ASR with client-reported latency
- Mock / Ollama / DomainForge LLM triage routes
- edge-tts server synthesis + browser speechSynthesis fallback
- REST and WebSocket transport with phase events
06AgentOps workbenchMissionsTracesEval Gates
AegisLoop AgentOps
AgentOps workbench — bounded missions, traces, eval gates, and human-gated ship.
Bounded missions, specialist handoffs, observable traces, source coverage scoring, FinOps estimates, Langfuse spans, and human-gated ship paths.
Decision
Missions over monolithic prompts — specialist handoffs with observable traces.
Signal
mission_gate CI suite · P50/P95 + failure rate on /api/metrics.
Limitation
Postgres persistence optional; mission history resets on in-memory backend.
What shipped: AegisLoop: mission fleets with eval gates →
- Mission orchestrator with specialist agent routing
- P50/P95 latency + failure rate metrics API
- VAP A2A delegation and AegisAI gateway on ship
- Golden eval CI gate via golden-eval-registry
07Content automationLangGraphHITLPublish
AI Content Factory
Governed content pipeline — research, multi-platform drafts, HITL, then publish.
Research → five platform drafts → HITL review → governed publish through AegisAI gateway, OAuth adapters, and scheduled cron pipelines.
Decision
AegisAI gateway blocks publish until policy allows — side effects never bypass governance.
Signal
Real LinkedIn/X OAuth publish path when tokens configured.
Limitation
Clerk keys required for full pipeline; invite-only signup in production config.
What shipped: AI Content Factory: governed multi-platform publish →
- End-to-end research and multi-platform draft generation
- Clerk auth with human approval gates before publish
- LinkedIn and X OAuth when tokens configured
- AegisAI gateway blocks publish until policy allows
08Self-improvementLoop EngineeringLangGraphMCP
LoopForge
Self-improving agents — clone repos, run pytest, patch, and open PRs.
Agent → Harness → Loops → Memory. LangGraph coding loop (Orchestrator · Review · Quality), ODAEU RAG tuning, and repo fix workflow: clone → pytest → patch → branch → PR — never pushes to main.
Decision
Harness + eval loops over bigger prompts — ODAEU tunes RAG on failure.
Signal
repo_fix suite in golden-eval-registry · never pushes to main.
Limitation
Requires GitHub token for repo fix loop; Groq API for agent inference.
What shipped: LoopForge: clone → pytest → patch → PR workflow →
- Repo fix loop: clone GitHub repo, run pytest, apply patches, open PR
- LangGraph agents: Orchestrator, Coding, Review, Quality, Memory, Self-improve
- ODAEU harness tunes hybrid RAG on eval failure + procedural memory
- Live API on Render · demo UI on Vercel · Groq + GitHub token
09LLM inferencePagedAttentionContinuous BatchingKV Cache
vLLM Architecture Lab
Interactive vLLM architecture explorer — PagedAttention, batching, KV budget, adapter chat.
Educational vLLM reference: PagedAttention, continuous batching, KV budgets, multi-LoRA economics — pairs with DomainForge adapter training (ADR-022).
Decision
Educational simulator before GPU spend — teams learn inference architecture without H100 budget.
Signal
Live simulator · KV budget calculator · DomainForge↔vLLM Path B (ADR-022 Accepted).
Limitation
Simulator + educational adapter swap — not CUDA multi-LoRA kernels or production SLAs.
What shipped: vLLM Architecture Lab: PagedAttention explorer →
- PagedAttention block allocator with swap and prefix sharing
- FCFS scheduler — waiting / running / swapped queues
- KV memory budget calculator (H100 + Llama-3)
- Live API on Render · architecture explorer on Vercel
- Educational multi-LoRA Path B — `/v1/chat/completions` + `/v1/adapters`
10Overnight intelligenceLangGraphCronEval Gate
Sentinel Brief
Overnight intelligence brief — nine sources, snapshot diff, eval gate, governed email.
Allowlisted HN, arXiv, and industry RSS → snapshot diff → executive brief → eval gate → AegisAI gateway email — autonomous inner loop, governed outer loop on mail only.
Decision
Autonomous inner loop, governed outer loop — only email send goes through gateway.
Signal
brief_gate eval · durable archives in /reports · nightly-smoke CI.
Limitation
Render cold start may delay first live run; archives prove report UX without a fresh cron.
What shipped: Sentinel Brief: governed overnight intelligence brief →
- Nine allowlisted sources — RSS/API first
- Snapshot deltas reduce noise vs full feeds
- Eval gate before email; gateway on send only
- Committed archives merged into GET /reports · nightly golden smoke
11Multimodal multi-LLMMulti-LLMMulti-AgentMCP
OmniForge
Multimodal ask with proven multi-LLM routing — waterfall shows which model ran each step.
Self-contained multimodal multi-agent multi-LLM platform — text, image, and voice fan out across agents and MCP tools; task-class routing with a live model waterfall and A/B proof.
Decision
Self-contained monorepo — no sibling runtime deps; routing is architecture, not a dropdown.
Signal
Live demo · /v1/ask waterfall · /v1/ask/ab · ADR-027
Limitation
Render cold start on free tier; mock fallback when cascade misses; clean omniforge.vercel.app may need SSO off.
- Multimodal ask — text · image/screenshot · voice transcript
- Planner fan-out across web/api/data/analysis/vision + MCP tools
- Task-class Multi-LLM Brain — Groq · OpenAI · Anthropic · Gemini cascades
- Model waterfall + A/B routed vs single · in-repo FinOps + export gate
12Interview practiceLLM-as-JudgeSystem DesignInterview Prep
AI Architect Practice Arena
Mock system design and behavioral interviews with dual LLM judges.
Sectioned system-design forms, STAR behavioral practice, and reasoning trade-offs — graded by OpenAI and Anthropic against Staff+/Principal rubrics. Bring your own API key; keys never touch our servers.
Decision
Per-question rubrics over generic chat — grading matches playbook depth.
Signal
139/140 live cases passed · judge disagreement shown, not averaged away.
Limitation
Requires user's OpenAI or Anthropic API key; no server-side key storage.
What shipped: Practice Arena: 35/35 interview playbook coverage →
- 35/35 interview playbook coverage across three rubric formats
- Dual-judge grading with disagreement surfaced as trust signal
- 139/140 live calibration cases passed across two independent runs
- BYOK architecture — zero org-side API cost at any scale
Eval discipline
golden-eval-registry — regression gates wired into real CI
Six suite kinds (RAG answer, harness QA, repo fix, mission gate, graph HITL, brief gate). Enterprise RAG and AegisLoop fail builds on regression — not just validate fixtures. Running one suite for the first time found and fixed a real bug in its own fixture.
Architecture Decisions
Why each layer exists — not just what it does.
Canonical ADRs and case studies live in the GitHub architecture portfolio— browse trade-offs before forking code. Architecture theses →
Canonical Essay
From Multi-Agent OS to Agent Governance
Why VAP and AegisAI are complementary layers — not competing products.
Read the full architecture storyFrom Multi-Agent OS to Agent Governance
Orchestration without governance is a liability.
VAP answers what agents should do. AegisAI answers what they are allowed to do. The portfolio ships both — wired together through gateway SDK, HITL overrides, and signed audit — because enterprise programs need both layers from day one.
Proof: Live integration: VAP notify · RAG ingest · Content Factory publish · AegisLoop ship
RAG is not retrieval infrastructure
It is an access-controlled intelligence layer.
Enterprise RAG filters by principal clearance and group membership before ranking — then hybrid retrieval, reranking, graph expansion, and citation traceability. Vector DB selection is an implementation detail; authorization-before-ranking is the architecture decision.
Proof: enterprise_rag_platform · enterprise-rag-platform-eta.vercel.app
AgentOps is how fleets survive production
Missions, not monolithic prompts.
AegisLoop models bounded missions with specialist handoffs, source coverage scoring, evaluation gates, FinOps estimates, and human-gated ship — the operating layer between orchestration platforms and production agent fleets.
Proof: aegisloop-agentops-workbench.vercel.app
Curriculum patterns teach the loops — platforms govern them
ReAct · Reflection · Plan-Execute · Multi-Agent · Swarm — each with a live trace viewer.
Five MIT-licensed curriculum stubs with animated trace demos on Vercel. Not production fleets — they map ideas into VAP / Content Factory governed graphs. Status tables mark stub LangGraph and no gateway.
Proof: 5 live trace viewers · Curriculum Agent Patterns series
Loop engineering is the next layer
Agents that improve after eval failure — not bigger prompts.
LoopForge implements Agent → Harness → Loops → Memory: LangGraph repo fix (clone → pytest → PR), ODAEU RAG tuning, and procedural memory — live on demo-omega-taupe.vercel.app + loopforge-api.onrender.com.
Proof: demo-omega-taupe.vercel.app · loopforge-api.onrender.com
2026 Agent Protocol Stack
Skills → MCP → Gateway → Observability
MCP for tool access, vpeetla-ai-skills for engineering discipline, AegisAI for side effects, trace-linked LLMOps (Langfuse/OTel) for evals on the same trace_id. ADR-007 documents how platform repos compose for production agent deployments.
Proof: vpeetla-ai-skills · ADR-007 · ORG_IMPROVEMENT_PLAN_2026
Free stack, production boundaries
Vercel + Render + Groq/Upstash/Qdrant — architecture discipline without enterprise budget on day one.
Every live demo runs on free-tier infrastructure with explicit service boundaries: UI on Vercel, APIs on Render, LLM via Groq, vectors via Qdrant Cloud, cache via Upstash — the same pattern I publish in Building Multi-Agent Systems on a Free Stack.
Proof: 15 live products on Vercel + Render free tier
10→2
Agent ops staffing reduction (targeted flows)
Multi-$M
Revenue & savings — payments, subscriptions, EDI
14 live
Reference systems with platforms
29 ADRs
Documented architecture decisions
Architecture Decision Records
Real decisions from production systems
ADR-001
Orchestration vs governance split
VAP + AegisAI as complementary layers — orchestration without governance is a liability.
VAPAegisAI
ADR-002
Authorization before ranking
RAG is an access-controlled intelligence layer, not a vector DB wrapper.
Enterprise RAG
ADR-003
Mission-based AgentOps
Bounded missions with eval gates — fleets survive production, not monolithic prompts.
AegisLoop
ADR-004
Gateway + HITL for side effects
Side-effecting tool calls require policy + human approval + signed audit.
AegisAIContent Factory
ADR-005
Reference stack on free tier
Vercel + Render + Groq — production boundaries without enterprise budget on day one.
Full stack
ADR-006
Loop harness over monolithic agent
Self-improvement belongs in harness + eval + memory — not bigger system prompts.
LoopForge
ADR-007
2026 agent protocol stack
Skills, MCP, gateway, and trace-linked observability as one four-layer stack, not ad hoc wiring.
Full stack
ADR-008
Real publish scope and invite-gating
LinkedIn/X get real OAuth publish; platforms with no public API get honest copy-draft export instead.
Content Factory
ADR-009
Auth gate on VAP routes
Chat, orchestrator, ingest, RAG, and thread routes now require a caller identity, not just a claimed one.
VAP
ADR-010
Auth gate on AegisLoop mission-run routes
Closed the same gap independently in both the FastAPI backend and the Netlify serverless function.
AegisLoop
ADR-011
AgentFinOps as a standalone service
Real cost governance needs one shared ledger, not per-repo fragments — built as its own repo, not logic bolted onto two existing ones.
AegisAIAegisLoop
ADR-012
Real FinOps metering wired into both consumers
A shared cost-governance service only matters once it's actually called — real usage now halts real agent dispatch in both AegisAI and AegisLoop.
AegisAIAegisLoop
ADR-013
Bidirectional MCP + real A2A discovery
A gateway that only gates outbound MCP calls is half a protocol — AegisAI now exposes governed tools via MCP too, and AegisLoop is the org's first client to actually discover-then-invoke VAP's real A2A surface.
AegisAIAegisLoopVAP
ADR-014
golden-eval-registry becomes a real CI gate
Fixtures existing and fixtures being correct are different claims — running one suite for the very first time against a real pipeline found and fixed a genuine bug in the fixture itself.
golden-eval-registryEnterprise RAGAegisLoop
ADR-015
Genuine hands-on AWS + GCP infra
Terraform files nobody ever ran aren't infrastructure experience — a real apply, a real budget breach against a real database, a real orchestrator run against a real load balancer, then a real teardown confirmed empty by each cloud's own CLI.
agent-finopsAegisAI
ADR-016
Ingestion data contracts + real lineage
Validation logic that gets computed and then discarded isn't a data contract — enterprise_rag_platform now rejects bad ingests instead of silently indexing them, and found a whole CI test file that had never actually run along the way.
Enterprise RAG
ADR-017
Interview playbook as a standalone repo
Most AI-architect interview prep is generic; this repo's 14 entries either cross-link to a real shipped decision in this org or say plainly that they don't.
ai-architect-interview-playbook
Case Studies
Architecture context, trade-offs, and outcomes
Agent governance
AegisAI — Agent Governance
Runtime control plane — gateway, HITL, signed audit
Includes live demo link →
Multi-agent orchestration
Venkat AI Platform
3 LangGraph orchestrators · 7 RAG strategies
Includes live demo link →
Knowledge layer
Enterprise RAG Platform
Authorization before ranking · hybrid retrieval
Includes live demo link →
AgentOps
AegisLoop AgentOps
Mission fleets · eval gates · FinOps · Langfuse
Includes live demo link →
Content automation
AI Content Factory
Research → drafts → HITL → governed publish
Includes live demo link →
Loop engineering
LoopForge — Self-Improving Harness
LangGraph repo fix → PR · ODAEU RAG tuning · MCP · memory
Includes live demo link →
LLM inference
vLLM Architecture Lab
PagedAttention · continuous batching · KV budget simulator
Includes live demo link →
Architecture essay
From Multi-Agent OS to Agent Governance
Why VAP and AegisAI are complementary layers
Lucid Motors
Enterprise Agentic AI
10→2 staffing in targeted supply-chain flows
Volvo Cars
Gulf Payments Modernization
Multi-$M annualized revenue impact
Volvo Cars
Subscription Revenue Platform
Durable recurring revenue platform
Volvo Cars
Supply Chain EDI Re-Platforming
Multi-$M annualized savings
Agent Patterns
5 patterns · 5 trace viewers
Mapped to VAP orchestrators — inspectable, not abstract.
ReAct
Thought → action → observation loops with bounded iterations and tool contracts.
Used in: VAP Deep Research orchestrator
Reflection
Critique-and-revise before delivery — quality gates for draft-heavy copilots.
Used in: VAP Research + Architecture pipelines
Plan-Execute
Explicit planning phase before tool execution — safer multi-step enterprise workflows.
Used in: VAP Architecture Review orchestrator
Multi-Agent
Orchestrator-controlled collaboration with reviewer gates before final output.
Used in: VAP Platform orchestrator + specialists
Swarm
Parallel specialist exploration with handoff-friendly agent boundaries.
Used in: VAP parallel asyncio bundles
Open Source
18 catalogued repos · one reference stack
Platform Repositories
View all on GitHubvllm-architecture-lab
vLLM architecture lab — PagedAttention simulator, continuous batching scheduler, KV budget API, and interactive 5-tab architecture explorer.
Live demo- BlockSpaceManager + prefix cache (educational PagedAttention)
- LLMEngine step loop with FCFS scheduler and preemption
vpeetla-ai-skills
Org-wide agent skills for Cursor and Codex — MCP, gateway, loop engineering, TDD, honest status tables, vLLM inference.
- 18 composable SKILL.md packages
- install.sh for Cursor + Codex (AGENTS.md, CONTEXT.md)
golden-eval-registry
Versioned golden fixtures for the governed agent stack — portable, reviewable eval contracts so Enterprise RAG, LoopForge, AegisLoop, and Content Factory can prove they don't regress, not just demo.
- 6 suite kinds: rag_answer, harness_qa, repo_fix, mission_gate, graph_hitl, brief_gate
- Dependency-light, provider-agnostic scorer — 2 of 6 kinds now gate real CI builds in Enterprise RAG and AegisLoop, failing on regression, not just validating fixtures
ai-architect-practice-arena
An LLM-as-judge mock-interview tool covering all 35 questions in the interview playbook -- sectioned system-design forms, full STAR practice for behavioral questions, and framework-plus-example for reasoning trade-offs -- graded by both OpenAI and Anthropic against each entry's own real Staff+/Principal rubric, bring your own API key, never stored on or sent to our servers.
Live demo- Full 35/35 playbook coverage across three rubric formats -- sectioned system-design (with a live Mermaid diagram input), full STAR behavioral practice, and reasoning-framework trade-offs -- as one persistent sidebar + form + results shell, not three separate tools
- Dual-judge grading (OpenAI + Anthropic) with per-section strengths/improvements feedback; judge disagreement surfaced as a real trust signal, not silently averaged away
loop-engine-agent-platform
LoopForge — self-improving agent harness. LangGraph repo fix → GitHub PR, ODAEU RAG tuning, MCP tools, procedural memory. Agent → Harness → Loops → Memory.
Live demo- Repo fix loop: clone → pytest → patch → branch → open PR
- LangGraph: Orchestrator · Coding · Review · Quality agents
aegisai-enterprise-agent-platform
Enterprise agent governance control plane — gateway SDK, policy violations, HITL approvals, signed audit, agent registry, and governed orchestrators.
Live demo- Monitor → Govern → Remediate control plane UX
- AI Gateway with OPA policy and human-in-the-loop paths
agent-finops
Real, enforced cost governance for AI agent fleets — usage metering, budgets, and breach signals as a shared service, wired into both repos that used to fake it.
- One canonical pricing table + real per-call token metering, not guessed cost
- Live in production: AegisAI's Website Build agents halt via a real kill-switch on breach; AegisLoop's research/content missions halt via a budget guard
venkat-ai-platform
Principal-architect multi-agent OS — 3 LangGraph orchestrators, 7 RAG strategies, loop patterns, and gateway-wrapped delivery channels.
Live demo- Platform · Deep Research · Architecture Review orchestrators
- Enterprise RAG adapter as 7th retrieval strategy
enterprise_rag_platform
Production RAG as a governed intelligence system — access-aware retrieval, context engineering, evaluation, guardrails, and operational decision records.
Live demo- Authorization before ranking, not after generation
- Hybrid retrieval with citation traceability
aegisloop-agentops-workbench
Mission fleets with evaluation gates — bounded missions, specialist handoffs, traces, FinOps, Langfuse spans, and human-gated ship paths.
Live demo- Mission orchestrator with source coverage scoring
- VAP delegation and AegisAI gateway integration
ai-content-factory
Multi-agent content pipeline — research, five platform drafts, HITL review, governed publish (real LinkedIn/X OAuth; other platforms export a copy-ready draft).
Live demo- LangGraph pipeline with human approval before publish
- Real LinkedIn/X OAuth + PKCE, invite-gated signup
Agent Pattern Library (GitHub)
ai-architect-interview-playbook
Staff+ interview study notebook — AI/ML system design, classic distributed systems, cloud architecture, coding, interview craft, and STAR prep — hellointerview-style depth with a live table/notebook UI.
multi-agent-system-pattern
Centralized multi-agent architecture with specialized roles, orchestrator control, shared context, and reviewer gates before final output.
react-agent-pattern
ReAct pattern for tool-using assistants with bounded iterations, schema-light tool contracts, and separation between reasoning and execution.
plan-execute-agent-pattern
Plan-then-execute agent workflow for decomposing complex tasks into reviewable steps before tool execution begins.
reflection-agent-pattern
Self-critique and revision loop for improving agent output quality before responses reach users or downstream systems.
swarm-agent-pattern
Swarm-style agent coordination for parallel exploration, specialist handoffs, and emergent task routing across agent roles.
Enterprise Delivery
Production delivery outcomes — enterprise context
Agentic AIRAGMLOpsGovernance
Enterprise Agentic AI Platform
Lucid Motors · Automotive Manufacturing · 2023–Present
Enterprise teams needed AI automation beyond demos — repeatable operational work with governance, evaluation, and observability.
Created the foundation for automating targeted supply chain workflows and reducing staffing intensity from 10 people to 2 in repeatable flows.
PaymentsStripeGIBRetail Commerce
Gulf Payments Modernization
Volvo Cars · Automotive & Global Commerce · 2019–2023
Regional payment coverage needed to support Gulf-market transactions with better gateway fit, reliability, and revenue enablement.
Enabled multi-million-dollar annualized business impact while strengthening the platform foundation for regional growth.
SubscriptionsRevenue SystemsAPIsPlatform
Subscription Revenue Platform
Volvo Cars · Automotive Digital Commerce · 2019–2023
Core product lines needed recurring revenue capabilities rather than relying only on transactional commerce patterns.
Established durable recurring revenue growth with stronger platform control over subscription lifecycle operations.
Supply ChainEDIModernizationCost Reduction
Supply Chain EDI Re-Platforming
Volvo Cars · Automotive Supply Chain · 2019–2023
License-heavy SAP and TrueCommerce EDI workflows created recurring cost, integration friction, and limited engineering ownership.
Generated multi-million-dollar annualized savings while improving adaptability for supply chain operations.
The intellectual rhythm behind the code — weekly architecture essays on Substack and this blog.
Architecture portfolio (GitHub)Read architecture writing