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Malik Hamza Shabbir
AI Engineering

AI Agents & Workflow Automation

I build production AI agents and workflow automation for ops-heavy SMBs and startups that need agents shipping real work, not demos.

Send your project brief Last updated June 10, 2026
In short

AI agent development is what I do for teams who need an agent in production, not a prototype that breaks on the second prompt. I build agents and automations with LangChain, LangGraph, n8n, and the OpenAI, Azure OpenAI, and Claude APIs. I handle tool calling, retries, logging, and human handoff. Solo, and I own the result.

What you get

Multi-step agents built with LangChain and LangGraph, with tool calling, state, and retries
n8n workflow automation connecting CRMs, inboxes, Slack, databases, and internal APIs
RAG-backed agents using pgvector or Pinecone so answers stay grounded in your data
OpenAI, Azure OpenAI, and Claude API integration with model routing and fallback
Human-in-the-loop handoff so the agent escalates instead of guessing
Structured logging, token tracking, and error alerts so you can see what the agent did
Stripe, PostgreSQL, MySQL, MongoDB, and Redis wired into agent actions and memory
Node and Python backends with typed APIs and webhooks the agent calls
Eval harness and guardrails to catch bad outputs before they reach customers

How I work

  1. 01

    Call and scope

    We get on a short call. I learn the workflow, the tools it touches, and what success looks like. I send a fixed quote and a clear scope, not an open-ended retainer.

  2. 02

    Build and ground

    I build the agent with LangChain, LangGraph, or n8n. I add RAG with pgvector or Pinecone, wire your APIs and databases, and connect OpenAI, Azure OpenAI, or Claude.

  3. 03

    Test and harden

    I run an eval set against real cases, add guardrails and retries, and set up logging and alerts. The agent escalates to a human when it is unsure instead of guessing.

  4. 04

    Ship and hand off

    I deploy to your stack, document how it runs, and give your team a runbook. I stay available for a fixed support window to fix anything that surfaces in production.

Ways to work together

Production AI agent in 3 to 4 weeks, fixed price

I build one agent end to end with LangChain or LangGraph, tool calling, RAG, logging, and human handoff. Scoped to a single workflow, deployed to your stack, fixed price after a short call.

n8n automation build, 1 to 2 weeks

I wire a back-office or sales workflow across your CRM, inbox, Slack, and database with n8n. Includes error handling, retries, and a runbook so your team can maintain it.

AI workflow audit, 1 to 2 weeks

I map your support, back-office, or sales workflows and score where an agent fits and where it does not. You get a build plan, a stack recommendation, and an honest cost estimate.

Agent reliability hardening, fixed scope

I take a flaky agent or automation and add evals, guardrails, retries, and logging. I cut hallucinations and silent failures so the agent is safe to run unattended.

Tech I use for this

LangChainLangGraphn8nOpenAI APIAzure OpenAIClaude APIPythonNode.jsTypeScriptpgvectorPineconePostgreSQLRedisStripe

Common questions

Q.How much does it cost to build an AI agent?

AI agent and automation work usually runs $175 to $300 per hour, or a fixed price per workflow. A single production agent often lands in a few weeks of build time. I give you a fixed quote after a short call once I know the workflow, the tools it touches, and how complex the logic is.

Q.What makes an AI agent production-ready instead of a demo?

A production-ready agent handles real edge cases, not just the happy path. I add retries, guardrails, structured logging, and human handoff so it escalates instead of guessing. I run an eval set against real cases before launch. Demos skip this, which is why they break the second week in production.

Q.Can you hire an AI agent developer for just one workflow?

Yes, that is how I prefer to work. I scope a single workflow, like support triage or invoice processing, and ship it end to end for a fixed price. Starting with one tight workflow keeps cost predictable and proves value before you commit to a larger automation roadmap.

Q.Do you work with LangChain, LangGraph, and n8n, or just one?

I work with all three and pick based on the job. LangGraph fits stateful multi-step agents with branching logic. LangChain suits tool-heavy single agents. n8n is best for connecting CRMs, inboxes, and databases without heavy custom code. I match the tool to your workflow, not the other way around.

Q.How do you stop the agent from hallucinating or taking wrong actions?

I ground answers with RAG using pgvector or Pinecone so the agent cites your real data. I add guardrails on tool calls, validation on outputs, and human-in-the-loop approval for risky actions. Logging and token tracking let you see every decision, so failures surface fast instead of staying silent.

Q.What do you need from me to start?

I need access to the workflow you want automated, the tools it touches, and a few real examples of inputs and expected outputs. A short call covers most of it. The clearer the examples, the faster I can build an eval set and ship an agent that handles your actual cases.

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Start your project

Tell me what you are building and I will reply within one business day with next steps. You talk to me, the person writing the code, the whole way through.