AI Agent Developer in Bangladesh — LLM Agents, RAG and Tool Calling

Agent design: deciding what the agent may do, expressing that as tools with strict schemas, and choosing between a single agent with tool calling, a planner-worker pattern, or a graph of specialised agents with handoffs. I use LangGraph for orchestration where state and branching matter, the OpenAI and Anthropic Claude APIs directly where they do not, and MCP servers to expose tools that other agents can share.

Retrieval: chunking and embedding your documents, help centre or database into pgvector, hybrid search with reranking, permission-aware retrieval so users only see what they are allowed to, and citations so answers can be checked. Evaluation: a test set built with you in week one, automated regression gates in CI, and tracing so every agent run can be replayed and costed.

An agent is only a business when it is packaged. My AI Agent SaaS packages wrap the agent in a Next.js product with a NestJS API: sign-in with email and Google, team workspaces with roles and invites, tenant isolation at the database layer, Stripe subscriptions with usage-based add-ons, per-user metering and limits, a streaming chat and agent UI, an admin panel, and CI/CD with tests. Contently AI and Virtual Client are both built this way, and CakeOrFake — an AI screening tool with subscription billing — shows the same pattern in a browser extension.

Starter is $899 and takes two to three weeks: one production agent with tool calling, prompt design with an evaluation set, auth, Stripe billing with one plan, usage metering and a Next.js dashboard. Professional is $2,499 over five to eight weeks: multi-step agent workflows, a RAG pipeline, multi-tenant isolation, team workspaces, three-plan billing with usage add-ons, LLM observability, guardrails and an admin panel. Enterprise is $3,999 over ten to sixteen weeks: multi-agent orchestration, model routing, enterprise RAG with permission-aware retrieval, SSO and audit logs, an automated eval suite in CI and a container deployment with autoscaling.

Global agency figures for the same work are $20,000-50,000 for an advanced agent and well over $60,000 for enterprise builds; US AI agent developers bill $60-150 an hour and up. Hiring an AI agent developer in Bangladesh directly gets you senior engineering at a rate that leaves budget for the API bill.

If users mainly need answers from your content, a RAG chatbot is enough, and my AI Integration packages from $499 cover it. If the product has to do work — create records, send emails, run a process across several systems, decide what to do next — you need an agent. If the work is a fixed sequence with no judgement, a plain automation is cheaper and more reliable than either. Part of the first call is telling you which one you are describing.

Week one is always design: tools, data sources, the evaluation set and a running-cost estimate. After that you get weekly demos of the agent doing real tasks against real data, a shared board, and a fixed call inside the UTC+6 overlap with your day. Contracts include an NDA and full IP assignment; payment is by milestone through Wise, Payoneer or bank transfer; the code and the model-provider accounts are yours.