Custom AI agents take a goal, plan the steps, and call the tools that finish the work.
Start here when a repeatable process needs somebody to read, decide, and update.
AI Agent Development Services
Fig AI Systems is an AI agent development company. Seven services, each covering one layer of a working agentic AI system. Most projects need two or three of them.
- Services
- Seven, covering every layer of a working agentic AI system
- Start with
- The bottleneck, not the technology
- Proven in
- Two products we built and own
What agentic AI development services cover
Agentic AI development services are the engineering that turns a language model into a system that completes real work. That means planning, tool use, integration, and evaluation loops.
The model is the smallest part of the job. Everything around it is where the work is - and where projects fail.
Two explainers cover the definitions: what agentic AI actually is and how AI agents differ from chatbots.
The seven services
Most projects combine two or three. A reliable agent needs something to retrieve from and something to be measured against, so those two rarely travel alone.
Conversational assistants that gather what they need in dialogue, call tools, and produce a real artifact.
Start here when your users prefer to describe a need in their own words.
Several specialized agents coordinate over one process, with routing, handoffs, and concurrency. Each step can be checked on its own.
Start here when one agent doing everything has become impossible to debug.
Retrieval, semantic search, and clustering systems that turn messy business data into usable context.
Start here when nobody can find the right information in time to act.
Agentic test harnesses where agents test agents, simulate real user behavior, and identify failures. Results feed back into the workflows.
Start here when agents already exist but nobody can show whether they improve.
Model Context Protocol servers expose the tools, data, and actions of an existing system through one interface. Any MCP-compatible AI client can call them.
Start here when one capability must reach several AI clients, or when an MCP app needs submission to Copilot, Claude, and OpenAI.
Self-learning systems record each run and compare it against the criteria for a correct result. That judgment feeds back into later runs.
Start here when a system stays exactly as good as the day it shipped.
How to choose a starting point
Choose the service that removes the first obstacle. Write the workflow down and the missing layer is usually obvious.
- A person repeats a read-decide-update loop all day: custom AI agent development.
- Users would rather describe what they want than fill in a form: agentic chat assistant development.
- One request has to fan out into many pieces of work: multi-agent orchestration and workflow automation.
- Nobody can search the documents or records that hold the answers: vector databases, retrieval, and clustering.
- Nobody can tell whether the agents already running are improving: closed-loop evaluation and testing.
- A capability must be callable from AI clients you do not control: MCP server development.
- Every improvement has to be hand-written by an engineer: self-learning systems.
Scoping questions are answered on the agentic AI and AI agent development FAQ.
What these services produce
We applied all seven to our own two products before offering them to anyone else.
Build-a-Dress is a made-to-order fashion marketplace. Its agentic chat assistant lets shoppers design a dress in conversation. A separate workflow classifies the design to produce a quote. It is a live business with more than 2,000 monthly active users.
Report Grader is an AI analysis product for education teams. It grades school reports against national and international standards. A human review gate lets inspectors step in.
Both are written up under products built by Fig AI Systems.
Frequently asked questions
Which of the seven services should a team start with?
Start with the service that matches the blocker. If nobody can find the right information, start with vector databases and retrieval. If a person repeats the same read-decide-update loop all day, start with custom AI agent development. Most projects use two or three services together.
Do these services replace the software a business already runs?
No. Fig AI Systems connects AI agents to existing systems through APIs and workflow handoffs. Teams keep working in the CRMs, ERPs, and internal tools they already use.
What does a first engagement with Fig AI Systems look like?
A focused pilot on a single workflow, before any wider build-out. Fig AI Systems maps the current process and agrees what a correct result looks like, then builds the agent together with the evaluation harness that checks it. A human review gate stays in front of decisions that matter.
What is an MCP server, and when does a business need one?
An MCP server exposes tools, data, and actions over the Model Context Protocol. That protocol is how AI clients reach capabilities outside themselves. A business needs one when a capability must be callable from AI clients it does not own. Fig AI Systems builds MCP servers and supports submission of MCP apps to Copilot, Claude, and OpenAI.