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AI agents

AI agent development

Agents that do real work in your systems, with guardrails, evals, and audit trails. LLM integration, RAG pipelines, and agentic workflows built for production, not demos.

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What is an AI agent?

An AI agent is software that uses an LLM to plan and take multi-step actions in your systems, rather than just answering a single question. Codiot builds agentic workflows and the LLM and RAG pipelines behind them, with the guardrails, audit trails, and evals that make an agent safe to run against real systems, not just a compelling demo.
Capabilities

What we build.

Agentic workflow design

Multi-step agents that plan, call tools, and take actions, scoped to what should and shouldn't run without human review.

RAG pipeline development

Retrieval systems that ground agent decisions in your actual documents and data.

Guardrails and permissions

Explicit boundaries on what an agent can do, with human-in-the-loop checkpoints for anything irreversible.

Audit trails and observability

A record of every agent action and decision, for debugging and compliance.

Evaluation frameworks

Automated testing that catches agent quality regressions before they reach production.

How it works

How it works with Codiot.

01

Scope the agent's boundaries

We define exactly what the agent can and can't do before writing the first prompt.

02

Build with guardrails from day one

Permissions, audit trails, and human checkpoints are part of the initial build, not bolted on after an incident.

03

Test against adversarial cases

Evals include the edge cases and misuse attempts an agent will actually encounter, not just the intended happy path.

Why Codiot

Stack we use, and why teams choose us.

OpenAIAnthropicLangChainVector databasesPython
  • ·Senior engineers only, no hand-offs to juniors mid-project
  • ·Overlap hours with US/EU time zones
  • ·Weekly demos and a single point of contact
  • ·Code you own, documented and tested
FAQ

Common questions, answered plainly.

What's the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent plans and takes multi-step actions in your systems, calling tools and APIs to actually complete a task, not just describe how to do it.
How do you keep agents from taking the wrong action?
Explicit permission boundaries, human-in-the-loop checkpoints for anything irreversible, and audit trails on every action taken, built in from the first version, not added after something goes wrong.
What is RAG and why does it matter?
RAG (retrieval-augmented generation) grounds an LLM's answers in your actual documents and data, instead of relying only on what the model already knows, which cuts down on confidently wrong answers.
How much does agent development cost?
It depends on the number of tools and systems the agent needs to integrate with. A focused single-workflow agent is a well-bounded build; we quote a fixed scope after discovery.
How do you test agent quality before launch?
Automated evaluation frameworks run the agent against a set of real and adversarial scenarios, so regressions are caught before users hit them.
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Explore related work.

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Let's talk about an AI agent.

Tell us what you're building. We'll reply within two business days with an honest take on scope, timeline, and cost.

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