Most AI agent demos never reach production because tool integrations break and outputs are unreliable. We design the workflow, build the agent and its tools, and test it against your real process so it completes the task end to end — with the code and a runbook handed over to your team.
Is this your problem?
Your team repeats the same multi-step process every day — research, data entry, triage, follow-ups.
You have seen AI agent demos, but none of them made it into production.
Your prototype agent loops, calls the wrong tool or fails silently.
You want automation that handles real work, not a chatbot that only answers questions.
Why this happens
Agent demos are easy; reliable agents are not. Production agents fail when tools are not integrated properly with your real systems, when there is no structure around how the agent plans and checks its work, and when nobody has tested it against the messy inputs it will actually see. Without guardrails, logging and clear stop conditions, an agent that looks impressive in a demo becomes unpredictable at work.
What we do
Map your process — inputs, decisions, tools and the output you need.
Design the workflow — what the agent decides and what stays rule-based.
Build the agent and its tools — APIs, databases, documents, email or CRM.
Add guardrails — validation, retries, logging and human approval where needed.
Test on real cases and tune until it completes the task reliably.
What you get
Working AI agent that completes your process end to end
Source code in your repository
Runbook covering how it works, how to run it and how to change it
Test results on your real examples
What we need from you
A description of the process, with 3–5 real examples
Access to the tools the agent must use (test or sandbox accounts are fine)
Your preferred LLM provider, if you have one
Common cases we handle
Lead research and enrichment agents
Document processing and data-entry agents
Customer support triage and reply-drafting agents
Agents built with LangGraph, OpenAI Agents SDK, Claude tool use, CrewAI or n8n
Pricing and turnaround
Starting price | From $149 |
Delivery | Typically 3 days |
Priority delivery | 12h for +50% |
Includes | Scope check, the work, deliverables and handover notes |
Every task gets a fixed price, confirmed after the free scope check. Your code and data are used only for this task, and we sign an NDA on request.
How it works
Submit your task. Tell us what you need and share the files or access listed above.
Free 30-minute scope check. An engineer confirms the scope and gives you a fixed price before any work starts.
We do the work. Your task is handled by Codersarts' own engineering team — not a freelancer marketplace.
Delivery and handover. You get the deliverables, a walkthrough of what changed, and time to ask questions.
Related tasks
Integrate OpenAI or Claude API — if you only need a single AI feature in your app.
Connect Third-Party API or Webhook — to connect the systems your agent depends on.
Fix RAG Answer Quality — if your agent answers from documents and gets them wrong.
Need more than a single task?
For a multi-agent system or an AI product built end to end, see Codersarts Build Solutions.
FAQ
How much does it cost to build an AI agent?
From $149 for a Deep Task. The fixed price is confirmed after a free 30-minute scope check, based on the number of steps and tools.
How long does it take?
Typically 3 days. Priority delivery is available for +50%.
Which frameworks do you use?
We pick what fits your stack — LangGraph, OpenAI Agents SDK, Claude tool use, CrewAI or n8n — and explain the choice.
Can a human approve actions before they happen?
Yes. We add approval steps for any action you do not want the agent to take on its own.
Is my data confidential?
Yes. Access is used only for this task, and we sign an NDA on request.
Prefer to learn it yourself? Explore hands-on courses at Codersarts Labs.