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AI Agent Development

AI agents with boundaries, built for real workflows

We build agents that read, decide and act inside your systems — with narrow permissions, clear limits and a human in the loop wherever a mistake would matter.

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What is an AI agent, and when is it worth building one?

An AI agent is software that uses a large language model to plan and carry out multi-step tasks by calling tools such as your APIs, databases or documents. It is worth building when a workflow is repetitive, rule-heavy and text-heavy, and when each step can be checked. ImliStack starts with one bounded workflow, measures it on real cases, and adds autonomy only where results justify it.

Is this for you

Where we usually help

If any of these sound familiar, a short call is the fastest way to find out whether we are the right fit.

  • Staff copy data between systems and chase follow-ups by hand
  • Support or operations teams answer the same questions from many sources
  • A chatbot demo impressed people but cannot be trusted with real actions
  • You want AI to act inside your product, not just chat beside it

What we deliver

AI Agent Development capabilities

01

Workflow discovery

Pick a bounded workflow with clear inputs, outputs and success criteria.

02

Tool and API design

Safe, narrowly scoped tools the agent can call — read and write separated.

03

Agent orchestration

Planning, retries, step limits and fallbacks using proven frameworks or plain code.

04

Human-in-the-loop

Approval steps before payments, emails or record changes.

05

Evaluation and logging

Test sets, traces of every decision and quality dashboards.

06

Data-aware assistants

Natural-language access to your databases and documents with sources shown.

How we work

A delivery approach you can follow

Written plans, visible progress and decisions recorded as we go — so nothing depends on memory.

  1. Pick one workflow

    Narrow scope with measurable value.

  2. Prototype with real data

    A working agent on representative cases.

  3. Evaluate honestly

    Measure accuracy and failure modes before rollout.

  4. Ship with guardrails

    Permissions, approvals, monitoring and cost limits.

Technology

Tools we use for ai agent development

We choose tools for your constraints, not our habits. See the full technology overview.

  • OpenAI API
  • Anthropic API
  • LangChain
  • Python
  • FastAPI
  • Node.js
  • Tool calling
  • PostgreSQL
  • MongoDB
  • Redis
  • BullMQ

Proof, not promises

We have built LLM services in Python with LangChain for AI-generated insights, transcript-to-action-item extraction, and a natural-language database assistant prototype.

See AI builds

FAQ

AI Agent Development: common questions

How do you keep an AI agent safe?

Agents get only the tools they need, read and write actions are separated, consequential actions require human approval, every step is logged, and hard limits cap steps and spend.

Single agent or multiple agents?

Usually one well-designed agent with good tools beats several cooperating agents. We add more only when a workflow clearly splits into independent roles.

Which models do you use?

We integrate hosted models from providers such as OpenAI and Anthropic, chosen per task for quality, latency and cost. We do not train foundation models.

Related services

All services

Next step

Have a product to build or a system to fix?

Tell us what you are working on. You will get a considered reply from an engineer — with questions, a suggested approach or an honest “we are not the right fit”.

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