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AI Agent Development

AI agents,
built to survive 2am.

Anyone can demo an agent that works once. Getting one that holds up under real users, real load, and real edge cases is a different job. I design and ship production-grade AI agents — orchestration, guardrails, cost controls, and evals — the layer between "it works in the demo" and "it works on a Tuesday at 2am."

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Why agents break

The demo lied to you.

An agent that calls one tool and returns clean JSON in a demo is not the same system as one fielding a thousand concurrent users with messy input, flaky APIs, and a cost meter running. Almost every agent that fails in production fails for the same handful of reasons — and none of them are the prompt.

What actually goes wrong

  • No retries or fallbacks. One bad tool call takes the whole run down.
  • Cost with no ceiling. A loop or a retry storm quietly triples the bill overnight.
  • Zero observability. When it misbehaves, nobody can see which step, which tool, which token budget.
  • No evals. Every prompt change is a coin flip; nobody knows what's live vs. tested.
  • Open scope. Prompt injection and tool over-permissioning turn a helper into a liability.

What production actually needs

  • Guarded tool calls — retries, timeouts, fallbacks on every external hop.
  • Cost controls — budgets, ceilings, and alerts before finance finds out.
  • Traced execution — every step, tool, and token visible and replayable.
  • An eval harness — you ship a change knowing it's safe, not hoping.
  • Scoped guardrails — least-privilege tools, injection defense, human gates where they matter.
How I build

Architecture first, then the agent.

I've built agentic systems from scratch and rescued ones that stalled at scale. The speed comes from already knowing where the traps are. A typical engagement starts with a two-week Discovery Sprint — scoping the agent, its failure modes, and the right architecture — then moves into a fixed-scope build or an embedded engagement.

Orchestration on LangGraph or LangChain. Reasoning on GPT-4o, Gemini, or Claude, chosen by task and cost profile. Retrieval on Qdrant or Pinecone. An LLM-as-judge evaluation layer so quality is measured, not asserted. The stack follows the workload — I don't marry a framework before I understand the problem.

Proof — 0→1, shipped in 30 days

An agentic pipeline that went from 40k to 1,000,000 items.

A client had no pipeline. I built the whole system: an agentic crawler on Cloud Run + Pub/Sub with a six-layer bot-bypass chain, a Qdrant vector store with Gemini embeddings, a LangGraph RAG pipeline with GPT-4o reasoning and a critic agent, and an LLM-judge evaluation framework to keep quality honest at scale.

40k → 1M
Items indexed
hours → 40 min
Crawl time
30 days
Demo to production
Questions

Common questions.

How much does custom AI agent development cost?
Most engagements start with a 2-week Technical Discovery Sprint to scope the agent, its failure modes, and the right architecture. From there it becomes a fixed-scope build or an ongoing embedded engagement. You get a clear number and roadmap before any build commitment — not an open-ended hourly meter.
What makes an AI agent "production-grade" instead of a demo?
A demo answers the happy path once. A production agent survives real users: retries and fallbacks on every tool call, cost ceilings and observability so bills can't spike silently, guardrails against prompt injection and scope creep, and an evaluation harness so you know a change is safe before it ships.
Which frameworks and models do you build on?
LangGraph and LangChain for orchestration; GPT-4o, Gemini, and Claude depending on the task and cost profile; Qdrant or Pinecone for vector search. The stack follows the problem — I don't marry a framework before I understand the workload.
Can you fix an agent that already works in the demo but breaks at scale?
Yes — that's the most common starting point. The Discovery Sprint audits what breaks (usually retries, cost, observability, or eval coverage) and returns a prioritized fix roadmap. Most fixes sit at the architecture boundary, not in the prompt.
Do you build the agent, or just advise?
Both are available. I take full technical ownership from whiteboard to production, or embed alongside your team as technical co-founder. If you have engineers, I set the architecture and guardrails and review the build.

Think we might be a fit?

I work with a small number of early-stage startups at a time. Tell me where your product is — if I'm not the right call, I'll say so.

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Related
Agentic Coding → Blog: Why AI agents fail at 2am → Fractional CTO vs Dev Agency → Blog: Agents that ship PRs →