Agentic AI Development with Malahim Haseeb
AI agents that do things, not just answer things
I build multi step AI agents with LangGraph and LangChain. The agent researches, decides, takes action, and loops back when something fails. It is not a single prompt in a chat box. I handle the graph design, tool calling, and state management, then wire it into your product backend.
Who it is for
Founders and product teams who want an AI feature that takes actions instead of just answering questions. Good for support automation, research assistants, data analysis agents, and internal operations bots.
What it covers
- Customer support escalation and resolution
- Research and summarisation agents
- Data analysis and reporting agents
- Internal operations automation
How it works
- 01
Scope the agent
We map the goal, the tools it needs, and the failure modes before any graph code.
- 02
Design the graph
I break the agent into nodes with clear responsibilities and state flow.
- 03
Build and wire
I implement the agent, tool calling, and the API that talks to your product.
- 04
Test and harden
I run evals and failure drills so the agent holds up in real use.
Questions
How long does it take to build an AI agent?
It depends on complexity. A single tool using agent is a small project, a multi step research agent with evaluation is a bigger one. I scope the build before committing to a timeline.
Do you build AI agents with LangGraph or LangChain?
I pick the framework per project. LangGraph for complex agentic systems where control flow and state matter, LangChain where the surrounding ecosystem fits better. The decision comes from the problem, not preference.
How do you test an AI agent before shipping?
I run evals on the agent graph, then failure drills with real inputs. If the agent loops or takes wrong actions, I add checkpoints before they reach users.
Can the agent take real actions in my product?
Yes. I wire the agent into your backend so it can call your APIs, write to your database, or trigger workflows. I design the action surface before the agent ever runs.
Can you take full ownership of an AI project?
Yes. Give me the requirements and I will figure out the architecture, build it, deploy it, and stay available after. I have shipped AI products solo for real companies. If there is a team, I can lead or fit into it depending on what is needed.
Can you integrate AI into an existing product?
Yes. I have worked with existing codebases and added AI features without breaking what was running. I read the code first, then design the AI layer around it.
What happens when the model or the data is not good enough?
I build fallbacks. Retrieval fallbacks, model fallbacks, confidence handling, and human escalation paths. An agent that loops forever or answers from the wrong context is a failure mode I design against.
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