Consulting
Fractional Head of AI
I embed in your engineering team: in the codebase, in the architecture reviews, in the sprint planning. I ship code alongside your engineers and own outcomes, not slide decks.
Three services. Pick what fits.
Building AI agents as products.
The model is maybe twenty percent of the work. The other eighty is the architecture between the model and the user: context engineering, state management, observability, graceful degradation. That's the part I do. See the full agent engineering methodology.
Multi-agent orchestration
frameworksLangGraph for deterministic control flow, OpenAI Agents SDK for delegation, Claude Agent SDK for codebase interaction. I pick the framework based on what the problem needs.
Voice & video AI
pipelinesReal-time voice agents for customer interactions. Video pipelines for KYC, document verification, and visual processing.
RAG systems
retrievalHybrid search with re-ranking, context window management, handling ambiguous queries and stale data. Sub-second responses at scale.
MCP server architectures
integrationConnecting agents to live infrastructure: databases, APIs, monitoring dashboards, so they operate on real data, not stale context.
Context engineering
disciplineDesigning what information agents see, when they see it, and how it's structured. The discipline that determines whether agents are reliably useful or reliably dangerous.
Observability & graceful degradation
reliabilityAgents that know when they're uncertain, escalate correctly, and fail without taking down the system. LangFuse tracing, structured logging, fallback chains.
Making your team measurably faster.
Not a workshop and a slide deck. I install the infrastructure in your codebase and run the first agent-native sprint with your team. See the full SDLC 2.0 methodology.
Spec-driven development
constitutionsCLAUDE.md constitutions, AGENTS.md cross-tool rules, three-tier agent boundaries. The specification infrastructure that makes everything else work.
Parallel agent workflows
worktreesGit worktrees, Claude Code, Codex, Agent Teams. 3-5 concurrent agents on independent tasks, each in isolation, PRs created for review.
Quality gates & hooks
enforcementPreToolUse and PostToolUse hooks that enforce linting, type-checking, and tests on every agent action. Hard gates, not suggestions.
Context engineering workshops
enablementTraining your team on Skills, memory systems, and the boundary policies that separate 3× teams from teams drowning in AI-generated debt.
Building the internal agent platform.
Deploying agents to production is one thing; running them well across teams, runtimes, and regulators is another. The “agent platform engineer” role for companies that don't have one yet.
Agent registry & manifests
source of truthOne YAML source of truth per agent: owner, runtime, model provider, data tier, tools (inline / MCP / Skill). Pydantic-validated. Powers the dashboard, governance reviews, and onboarding.
Multi-runtime catalog & deployment
infrastructureVercel AI SDK, Claude Agent SDK, AWS Lambda, Pulumi-managed GKE: all in one registry. Infrastructure-as-code for the long-running agent fleet (Kubernetes manifests, GitHub Actions via Workload Identity Federation, secrets in Secret Manager). Teams keep their preferred runtime; the platform unifies visibility.
Cross-agent observability
tracingOpenTelemetry + Langfuse wired identically on every deployment. Trace one customer's flow across N agents and runtimes. Span attribution by firm, agent, and domain.
Capability registry & graduation
reuseInline TS function → MCP server → Claude Skill, then published to a curated registry that any agent on any runtime can search and install via MCP. So a tool built for one team isn't reinvented by the next.
Data tiers & boundaries
governancePublic / internal / confidential / regulated: declared in manifest, enforced in routing. Maps to model selection so sensitive data never reaches a public-cloud frontier model by accident.
Internal dashboard
visibilityFastAPI dashboard serving the cross-firm view: who owns which agent, which model, last trace, cost trend, data tier. The single shared source of truth you can hand a new joiner and a regulator alike.
The stack
The tools I reach for, organized by what they do.
agent frameworksLangGraph, OpenAI Agents SDK, Claude Agent SDK
coding agentsClaude Code, Codex, Agent Teams
orchestrationCLAUDE.md, AGENTS.md, Skills, Hooks
infrastructureMCP Servers, Azure AI Foundry, Vercel
platformPydantic manifests, FastAPI dashboards, multi-runtime registry
deploymentPulumi, Kubernetes / GKE, AWS Lambda, GitHub Actions
voice & videoVapi, Deepgram, ElevenLabs, Tavus, Twilio
observabilityLangFuse, LangSmith, OpenTelemetry
Proven outcomes
From shipped systems, not slideware.
KYC hours → minutes
LangGraph video agent replaced a manual verification pipeline.
Campaign creation 2 weeks → 10 min
GenAI pipeline automated what took a team of designers.
3×retention lift
Deep RL personalization engine, 5M+ daily predictions.
1,000+monthly autonomous interactions
Voice agents handling real customer calls without human oversight.
Sub-second RAG responses
Hybrid search with re-ranking across ambiguous product catalogs.
$20M+annual transactions
AI pipeline processing 1B+ events monthly at scale.
Why me, not a consulting firm
The big firms are spinning up agentic AI practices. They'll send you three juniors supervised by a partner who has never deployed an agent.I've been on the other side of this. The juniors are good; the supervision isn't the bottleneck. The bottleneck is that no one on the team has shipped an agent that handled real money at 3am.
I've been building production AI for 14+ years: three startups, 40 under 40 Data Scientist, published researcher. And I spent years on the investor side evaluating 30+ AI startups. You get someone who has already made the mistakes, not someone learning on your timeline.
Tell me what you're building and where you're stuck: raman.shrivastava.7@gmail.com