AI Agents Development (LangChain, AutoGen, CrewAI)

I build production AI agents on LangChain and LangGraph, Microsoft AutoGen, and CrewAI — autonomous and semi-autonomous systems that plan, call tools, and complete multi-step work without a human driving every click. Typical builds include function/tool calling against your internal APIs, multi-agent orchestration where a researcher, writer, and reviewer agent collaborate on one task, persistent agent memory so context survives between sessions, and guardrails plus evals so the agent stays inside the rules you set.

Agents run on OpenAI (GPT-4.1, GPT-4o) or Anthropic Claude models — I benchmark both on your actual workload and pick per-task, since reasoning-heavy steps and high-volume extraction steps rarely want the same model. I connect agents to the systems UAE companies already run: CRMs like HubSpot and Zoho, WhatsApp, Google Workspace, and internal REST APIs, so the agent reads and writes real business data instead of living in a demo notebook.

For Dubai, Abu Dhabi and Sharjah businesses — free-zone and mainland SMEs alike — the case is headcount math: an agent that triages inbound enquiries, drafts quotes from your price list, or reconciles orders across two systems routinely replaces 20–40 staff-hours a month, which at UAE salary levels pays back the build cost in AED within the first quarter.

How it works

1. 1
   Workflow mapping call

   We break the target process into steps and decide which ones an agent can own, which need a human approval gate, and which tools the agent must call.

2. 2
   Prototype agent in week one

   A working agent against a copy of your data, so you can test tool calls and judge output quality before the full build.

3. 3
   Hardening and evals

   I add guardrails, retry logic, and an eval set built from your real cases, then iterate until pass rates hold steady.

4. 4
   Production rollout

   Deployment to your infrastructure with logging and alerting, a handover session, and 14 days of post-launch support.