AI agent development
AI agent development services.
Agents that hold up in production.
IrenicTech provides AI agent development services for teams that have a demo and need a product. We build agents with real tools, MCP servers, evaluation sets and guardrails, wired into the systems you already run. The decision logic of the agents we have delivered is published below, so you can judge the work before you talk to us.
The stack
What we build agents with
Who it is for
Who our AI agent development services are for
Teams with a stalled pilot
The prototype impressed a room and never reached real users. It needs an evaluation set, tighter access and a way to see why it did what it did.
Operations with judgment in them
Intake, qualification, scheduling or support, where a fixed chain of triggers breaks on the cases nobody wrote down.
Software companies whose customers use agents
Your buyers want their agents to drive your product. That takes an MCP server built with the same care as your public API.
Regulated and high-stakes work
Answers have to come from approved documents, with citations or a stated refusal, and a person has to approve anything the system cannot undo.
What we build
Agents, their tools and the controls around them
We choose the framework per job. LangGraph, the Claude Agent SDK, the OpenAI Agents SDK and CrewAI are all on our stack, with models from OpenAI and Anthropic behind a boundary, so a better model is a swap rather than a rewrite.
Single-purpose agents
One agent, one job, a narrow set of tools and a stated stopping condition. Most of the value in agents comes from here, so this is where we start.
Multi-agent pipelines
Several agents only when the work needs it, each with a role you can state in a sentence. In one delivered system an extraction agent hands to a validation agent, so unvalidated input never reaches the database.
Voice and chat agents
Agents on phone lines, WhatsApp and web chat, built with tools such as Vapi, Retell AI and Twilio, that write each outcome back to your systems.
Retrieval from your documents
Answers restricted to verified sources, with citations, and a defined fallback instead of a guess when nothing relevant is found. We have shipped this on Pinecone and on Qdrant.
MCP server development
Your system's tools and data exposed through the Model Context Protocol, with scoped credentials, so any compatible agent can use them and none can do more than you allow.
Evaluations and guardrails
An evaluation set built from real cases before capability is added, least-privilege access, human approval on irreversible actions, output validation and a full log of what was called and why.
This page is the narrow job: an agent that does real work. If you need a whole product designed around AI, that is our AI-native software development practice.
Proof
Agent systems we have delivered
Each is a delivered system, published as a full teardown: every path and the condition that fires it. Client names and measured figures stay out unless a client has cleared them.
Regulatory compliance · Teardown
A compliance line that answers from the documentation, or says it cannot
A PIN-gated voice agent that answers regulatory questions from verified documentation only, and has a defined fallback when nothing relevant is on file.
Read itLegal · Teardown
A case description in, a finished video out, learning from corrections
A reasoning agent that answers strictly from firm documents and returns citations. A correction reaches the knowledge base only after a person approves it.
Read itEducation · Teardown
Ten-plus businesses on one workflow, and nothing crosses between them
Extraction, validation and intent-scoring agents on WhatsApp, serving more than ten businesses on one workflow, with tenant identity as a filter on every retrieval.
Read itHealthcare · Teardown
From first contact to attended visit, with no manual coordination
A voice agent and a chat agent feeding one pipeline, with HIPAA-aware handling and an audit trail of every automated action.
Read it
How it works
How an AI agent engagement runs
We start narrow on purpose, because a small slice in front of real users teaches more than a large plan. A narrow agent on one workflow with an existing API is a matter of weeks. A platform where agents operate across several systems is a different order of work, and we quote it after discovery. The thinking behind this is in our guide to AI agent development.
01
Pick the workflow
We choose the one job where an agent removes real friction, where you can tell afterward whether it worked, and where being wrong is recoverable.
02
Build the evaluation first
Real cases from your business become the test set, so every later change gets a score rather than an opinion.
03
Build the narrow slice
Tools, credentials, guardrails and logging go in with the first version, not after it.
04
Ship to real users
A small group uses it early enough that what happens can still change the design.
05
Hand over or widen
You get the code, the evals and the runbooks. Then we widen the scope, or your team does.
AI agent development: common questions
What do AI agent development services include?
Choosing the workflow, building the evaluation set, building the agent and its tools, connecting it to your systems, adding guardrails and logging, and shipping it to real users. You keep the code, the evals and the runbooks. We can also build the MCP server that lets agents reach your product.
How do I choose an AI agent development company?
Ask to see delivered decision logic rather than a demo. Ask how they evaluate an agent before and after a change, what the agent can do without a person, and what happens when it is wrong. A team that cannot answer those has probably not run an agent in production. Our answers are in the teardowns on this page.
What is MCP server development?
The Model Context Protocol is an open standard for exposing tools and data to AI agents. MCP server development means wrapping your system in a server that any compatible agent can drive, with scoped credentials and logging, because an agent is an untrusted user of your product.
Do you build enterprise AI agents?
Yes. Enterprise AI agent development adds least-privilege access, human approval on irreversible actions, full logging and integration with the systems you already run. Our published system for Elementor, for example, ends every content workflow at a human approval.
How much does AI agent development cost?
Price varies with scope. Productized sprints are fixed-price, and full platform builds are quoted after discovery, once we have seen the workflow and the systems it touches.
How long does it take to build an AI agent?
A narrow agent on one workflow with an existing API is a matter of weeks. A system where agents operate across several platforms, with real compliance obligations, takes longer, and we give you the timeline once the scope is clear.
Tell us which workflow you have in mind
We will give you a straight answer on whether an agent belongs there, and what it would take to get one into production.
Book a discovery call