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.

Review an agent use case

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.

Scope the agent's boundaries
We define exactly what the agent can and can't do before writing the first prompt.
Build with guardrails from day one
Permissions, audit trails, and human checkpoints are part of the initial build, not bolted on after an incident.
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

Is this right for you?

When it fits, and when it doesn't.

Signs you need this

·A workflow needs multi-step reasoning and tool use, not a single prompt.
·Staff spend hours on repetitive research, triage, or coordination tasks.
·You need an agent that acts in your systems, safely and with oversight.
·Earlier chatbot attempts could not actually complete tasks.

What's included

·Agent design with tools, guardrails, and human-in-the-loop where needed.
·Integration to the systems the agent reads from and acts on.
·Evaluation, monitoring, and safety controls.
·A path from a narrow proof to production.

What's not

·Autonomy without oversight on high-risk actions, which we deliberately gate.
·Model usage fees, billed by the provider.
·Decisions the business is not willing to delegate to software.

When this isn't the right fit

·A deterministic script or simple automation does the job more reliably.
·The task has no tolerance for the occasional mistake and no room for review.
·The systems the agent would act in have no usable interface yet.

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.

Explore related work.

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