What does AI agent consulting include?
I start AI agent consulting work with the job the agent should do: which tasks it takes, which systems and documents it needs, what it may do without asking, and what counts as finished. Then I build the smallest version that does that job, run it on a set of real tasks, and fix what fails before it goes near production. If a target doesn't look realistic, I say so early.
The build is usually Python and FastAPI, with LangChain or LangGraph on the OpenAI API, the Claude API, or Ollama when the model has to stay local. Agents reach your systems through tool calling or MCP servers, answer from your documents with retrieval that cites its sources, and run as n8n workflows or inside Claude Code. Deployment is Docker and Kubernetes on GCP, Azure, AWS or your own machines.
| Stage | What we check or deliver |
|---|---|
| Scope | The job, what counts as finished, and what the agent may do without asking |
| Systems | The APIs, documents and accounts the agent needs, and how it reaches them |
| Test set | Real tasks with their expected results, written down before the build |
| Build | Tools, retrieval and prompts, in code the next person can maintain |
| Evaluation | Repeated runs of the test set, with every failure reviewed |
| Deployment | The agent running where it will live, with its configuration documented |
What kinds of AI agent development do you take on?
These are the kinds of work my AI agent consulting services cover.
| Work | What it does |
|---|---|
| Tool-using agent | Calls your APIs and picks the next step from what comes back |
| RAG assistant | Answers from your documents through embeddings and a vector database, and cites the passages it used |
| MCP server | Gives an agent such as Claude Code a defined set of tools for one of your systems |
| Claude Code setup | Instructions and skills that make a coding agent follow your team's procedures |
| n8n automation | A fixed workflow with model calls where judgement is needed, connected to tools you already run |
| Computer-use agent | Operates a logged-in browser and desktop where there is no API |
Some requests don't need an agent. If the steps are the same every time, an n8n workflow is easier to test than an agent that picks its own steps. If the model lacks your facts, retrieval is the first thing to try. If it has the facts but gives answers in the wrong shape, a fine-tuned smaller model can sometimes do the job for less money.
Can I see AI agent work you have built?
Client agents stay private, so the client result I can state is short: I have built RAG assistants over private company data that answer with citations. In an Upwork review, Nodirbek H. called me "a strong Agentic developer that can automate anything."
The parts you can inspect in full are public. AI Memory is a self-hosted memory service for AI agents, with its source on GitHub. It embeds text and images with CLIP and audio and video with ImageBind, stores the vectors in Qdrant, and serves them through a FastAPI service with authentication and tenant isolation. Memories fade along a forgetting curve. The case study also states a limit: the code does not align the coordinate systems of the two embedding models, so I don't claim reliable text-to-audio or text-to-video retrieval from it without a measured test.
I run Claude Code with 71 skills in my own agent repo, for jobs such as reviewing merge requests and publishing this site, and I measured when Claude Code loads a skill by itself. I have also built a computer-use agent that operates a real, logged-in browser and desktop. When a CAPTCHA or login wall appears, a person clears it.
How do you test whether an AI agent is reliable?
I run the same tasks many times and count the results, because one good demo says little about the next run. In the skills test I gave one skill six different frontmatters and sent each version six requests three times: 108 runs on Claude Code 2.1.310, each in a fresh folder. The skill loaded on 15 of 15 matching requests when its name or its description said what it does, and on 5 of 15 when neither did. That was one skill among about a dozen others on one model, so treat it as a direction for your own setup.
The same test showed a failure to plan for in any agent that changes things. A skill marked manual-only was never loaded, yet in all 15 runs Claude went straight to sorting the files itself, without the skill's rules, one of which was never to delete anything. A rule that must hold every time belongs in a hook, which is what Anthropic's documentation recommends when Claude skips one.
What do I need to send to scope an AI agent project?
Send what you want the agent to do and what you would count as finished. Add the systems it has to use (APIs, databases, documents, the apps people use for the job today), a set of real tasks with the results you expect, where it has to run, and which actions need a person's approval. If you already have an agent or workflow, its failures are the most useful thing you can send.
There is no useful fixed price or schedule before that review. Connecting the tools and testing on real tasks can take more work than the first version, so the quote covers both as well as the build.
How do I start working with an AI agent consultant?
Book a consultation with the button below. Bring the job and the systems it touches, and I'll tell you how I would approach it and roughly what it takes.
Frequently asked questions
How much does AI agent consulting cost?
I quote after seeing the job, the systems the agent has to use, a sample of real tasks and what you would count as finished. Connecting the tools and testing on real tasks can take more work than the first version, so a description of the idea alone is not enough for a useful estimate.
Do I need an AI agent, or is a simpler automation enough?
If the steps are the same every time, a workflow with a model call at the points that need judgement, built in n8n for example, is easier to test than an agent that chooses its own steps. An agent fits when the next step depends on what it finds. I tell you which one I would build before writing any code.
How do you make sure an AI agent is reliable?
I run the same set of tasks many times in fresh setups and count the outcomes. In my Claude Code skills test, 108 runs showed a skill loading on 15 of 15 matching requests when its name or description said what it does, and on 5 of 15 when neither did. For your agent, the test set comes from your own tasks.
Can an AI agent run on our own servers?
Often. I use Ollama when a model has to stay local, and the memory service I published is self-hosted, with authentication and storage isolated per tenant. I deploy with Docker and Kubernetes on GCP, Azure, AWS or on-prem machines.
Can an AI agent work in a system that has no API?
Sometimes. I have built a computer-use agent that operates a real, logged-in browser and desktop, and when a CAPTCHA or login wall appears, a person clears it. An API is faster and easier to test when one exists, so I look for one first.
How long does an AI agent project take?
The first working version is only one part of the schedule. I need to define the job and its pass condition, connect the tools and data, build a test set from real tasks, run it repeatedly, fix the failures and test the agent where it will run. I can estimate the timeline after seeing the systems and a sample of the tasks.