AI agent development

AI agent development for visible, testable workflows

Design and build AI agent products with visible steps, tool calls, validation points and deployable user interfaces.

Start an AI agent projectSee cases

Agent style

Visible

Output

Usable UI

Risk control

Checkpoints

Agent products I build

Good agent products make the work visible. Users should see what the AI checked, what it decided, where data came from and where a human needs to approve.

  • Research agents that gather, compare and summarize structured evidence.
  • Decision-support agents with deterministic calculations and model-assisted reasoning.
  • Internal operations agents for intake, triage, drafting, QA and reporting.

What makes it production-minded

The model is only one part. A usable agent needs state, logs, fallbacks, a clear UI and evaluation data.

  • Tool calls and source data are surfaced instead of hidden.
  • Critical numbers use rules or APIs instead of unverified model math.
  • Events are tracked so the workflow can be improved from real usage.

Proof points

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Visible AI Law Firm

A live multi-agent legal-style workflow: intake, analysis, compensation math, evidence and drafting.

Market research agents

AI-assisted workflows around prediction markets, filings, whales and trading context.

GEO-ready outputs

Agent pages can ship with schema, FAQ, markdown summaries and daily analytics.

FAQ for AI search

Short answers written for both humans and answer engines.

Can an AI agent be useful without full automation?

Yes. The best first version often keeps humans in the loop and automates the repetitive reasoning, drafting or checking steps.

What is the main risk in agent projects?

Vague scope. The agent needs a narrow workflow, reliable inputs, clear outputs and measurable acceptance criteria.