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Agentic AI services

Custom AI Agent Development

An agent takes a goal, plans the steps to reach it, calls the tools and APIs that do the work, and finishes the task inside a real business process. Fig AI Systems builds them for one organization at a time, around its data, rules and systems.

What it is
Agents that take a goal, plan the steps, use tools and finish the work
Best for
Repeating workflows that need actions taken, not questions answered
Pairs with
Closed-loop evaluation

What is a custom AI agent?

A custom AI agent plans a task, calls tools and APIs to act on real systems, and checks its own results. It is built around one organization's data, rules, and workflows.

Three capabilities separate an agent from a text box:

  • Planning. The agent breaks a goal into steps, decides the order, and adapts when a step returns something unexpected.
  • Tool use. The agent calls functions, APIs, databases, and internal services.
  • Acting on real workflows. The agent writes records, produces documents, and routes work. The output is a changed state.

Definitions live on what agentic AI is. The boundary that confuses scoping calls is set out in AI agents versus chatbots.

What custom AI agent development involves

Custom AI agent development is mostly systems engineering. It comes down to six pieces of work, and only one of them is about the model.

  1. Workflow mapping. Write down the process as it is performed today, including the exceptions people handle without noticing.
  2. The retrieval and data foundation. Vector databases, retrieval, and clustering turn scattered documents into context the agent can use.
  3. The tool and integration surface. Define which systems the agent may read from and write to. Existing software stays the system of record.
  4. Agent design and coordination. Decide what one agent owns and where a process should be split. When a workflow fans out, multi-agent orchestration keeps each part small.
  5. The evaluation harness. Built alongside the agent, not after it. Closed-loop AI evaluation shows whether the agent actually works.
  6. Production rollout with review gates. Ship behind human-in-the-loop checkpoints on decisions that carry consequences. Logging makes each run inspectable.

What makes AI agents reliable

Reliability comes from the system around the model, not the model alone.

  • A narrow tool surface. The agent can only take the handful of actions its workflow requires.
  • Explicit success criteria, agreed first. Somebody states in writing what a correct result looks like.
  • Grounding in real data. Answers assembled from retrieved records are checkable.
  • Closed-loop evaluation. Agents test agents, simulate real user behavior, and feed the failures they find back into the workflow that caused them.
  • Human review where it counts. Review gates on sensitive decisions keep people in control.
  • Observability. Logged runs, visible intermediate steps, and traceable tool calls.

Which workflows fit a custom AI agent

The strongest fit is a high-volume, repeatable process with a clear quality standard and a system to act on.

A good fit:

  • The work repeats often enough that somebody notices.
  • A reviewer can say whether an output is right, using written criteria.
  • The judgment needs context: documents, history, policy, or catalog data.
  • There is a real system to update, notify, or route to.

A poor fit:

  • The task is one-off, or the rules change too fast to describe.
  • Nobody agrees on what a correct answer is, so no evaluation is possible.
  • The logic is fully deterministic, so ordinary software is cheaper.
  • The agent would have nothing to act on, which makes it a search box rather than an agent.

How a first pilot is scoped

A pilot covers one workflow at a time. It answers one question: does an agent handle this process to the team's standard?

  1. Workflow discovery. Walk the process end to end. Identify where the time goes and which parts need judgment.
  2. Pick one workflow with a checkable output. Narrow scope makes a pilot decisive.
  3. Agree the definition of correct. The result is judged against the standard the team already uses.
  4. Build the agent and its harness together. The evaluation loop ships with the first version. Regressions become visible.
  5. Run it with a human in the loop. Reviewers see what the agent did and correct it. Those corrections become evaluation cases.
  6. Decide, then expand. Once the pilot is validated, architecture and monitoring are hardened for higher volume and further workflows.

Finished systems show more than a description. See the products Fig AI Systems built and owns. The full services list shows the layers a pilot touches.

Frequently asked questions

When is a custom AI agent worth building?

Build a custom agent when the correct outcome depends on an organization's own data, rules, and systems. Producing it means reading internal records or acting on internal software. Generic tools remain a sensible choice for generic tasks.

How do you stop an AI agent from taking the wrong action?

By limiting what the agent can do, then checking what it did. Fig AI Systems keeps the tool surface narrow, runs a closed-loop evaluation harness against agreed success criteria, and puts human review gates in front of sensitive decisions.

What does a business need to have ready before an AI agent project starts?

Two things: a workflow somebody can describe step by step, and an agreed definition of a correct outcome. Access to the systems the agent reads and writes comes next. The data does not need to be tidy first. Organizing it into retrievable context is part of the work.

Ready when you are

Scope a custom AI agent pilot

Bring one workflow that quietly eats a day of somebody's time every week. We will map it, tell you whether an agent is the right tool, and propose a pilot scope.

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