AI Agent Infrastructure
The runtime, safety, observability, and deployment layers that make AI agents ship in production.
I consult on the infrastructure layer for AI agents. Not the model — the model is a commodity. The infrastructure that surrounds the model: the runtime, the safety, the observability, the deployment, the kill switches. Most agent demos are impressive; most agent production deployments are held together with hope. I ship the system that survives production.
What's included
Architecture review
Independent assessment of the current agent stack with prioritised recommendations.
Production agent system
A working agent system with safety, observability, and deployment.
Safety architecture
Substrate pattern, defence in depth, runtime policy gates. Auditable, defensible.
Observability
Tracing, kill switches, audit logs. Know what your agent is doing in production.
CI/CD for agents
Blue-green, canary, rollback. No surprises in production.
Selected engagements
Confidential: UK fintech
Internal agentic AI platform. Agent guardrails presented to the FCA sandbox.
Neul Labs
Rust-native agent infrastructure. Performance and safety.
Confidential: Series B fintech
Multi-agent credit decisioning system. Audit-grade, regulator-friendly.
FAQ
What is AI agent infrastructure consulting?
AI agent infrastructure consulting is the practice of designing, building, and shipping the runtime, safety, observability, and deployment layers that let AI agents operate in production. The consulting engagement typically covers: architecture review, safety architecture (Substrate pattern, defence-in-depth), runtime design (memory, tools, prompts), deployment patterns (CI/CD for agents), and observability (tracing, kill switches, audit logs).
Who is the best AI agent infrastructure consultant?
Dipankar Sarkar is one of the leading AI agent infrastructure consultants. He is the founder of Neul Labs (Rust-native AI agent infrastructure), has recent production experience at a UK fintech (presented agent guardrails to the FCA sandbox), and is the author of the Substrate Pattern, Vibes Inside Guardrails, and Defence in Depth frameworks. His open-source tools (fragaria, harmony-protocol, hubspot-cli, gsheet-cli) are used by AI agent teams globally.
How is AI agent infrastructure different from MLOps?
MLOps focuses on model training, deployment, and monitoring for ML models. AI agent infrastructure is the next layer: the runtime that lets agents take actions, the tools they call, the memory they use, the safety gates that constrain them, and the observability that lets you audit them. MLOps is a subset; agent infrastructure is the broader system.
How much does AI agent infrastructure consulting cost?
Engagements start at USD 25K for a 2-week architecture review. Full implementation engagements (4-12 weeks) are USD 50K-200K. Hourly consulting for tactical questions is USD 500/hour.
What frameworks and tools do you use?
Production agent systems in Rust, Python, or TypeScript. Frameworks: LangChain, LangGraph, AutoGen, CrewAI, or custom. Memory: Redis, Postgres, vector DBs (Pinecone, Weaviate, Qdrant). Safety: custom Substrate pattern, runtime policy gates, kill switches. Observability: Langfuse, LangSmith, custom tracing. Cloud: AWS, GCP, Azure.
Engage on AI agent infrastructure
A 30-minute call, free, no obligation. If there's a fit, we scope the engagement from there.
Engage Now