AI agents
AI Agent Development with LangChain and LangGraph
An AI agent is software that can decide what to do next, use tools and finish a multi-step task on its own. I design and build AI agent systems with LangChain and LangGraph for research, support, analytics and store management, with guardrails so they stay useful and under your control.
Chatbot, workflow or agent: choosing the right one
The word agent is used loosely, so it helps to separate three things. A chatbot answers questions in conversation. A workflow follows a fixed sequence of steps, sometimes with an AI step inside. An agent chooses its own sequence of actions, calling tools and deciding what to do after each result.
Agents are powerful but less predictable, so they are not always the right answer. Many business problems are better solved with a fixed workflow that includes an AI step. I will tell you honestly which approach suits your problem, because a simpler design is cheaper to run and easier to trust.
How I build agents
I use LangGraph to define the agent as an explicit graph of steps, with shared state, branching, retries and clear stopping points. That makes behaviour easier to test and debug than a single long prompt. LangChain provides the integrations for language models and for the tools an agent can call, such as web search, databases and your own APIs.
For larger systems I split the work between specialised agents, each with a narrow job and a limited set of tools, coordinated by an orchestrator. Narrow agents make fewer mistakes and are easier to improve. Actions with real consequences, such as publishing content or changing prices, can be set to wait for human approval.
A real example: a multi-agent store manager
My AI E-Commerce Store Manager uses LangGraph and LangChain to orchestrate specialised agents for product research, SEO, listing generation, customer support, analytics and Shopify store management. The code is public on GitHub, and you can read the problem and solution in the portfolio case studies.
Keeping agents reliable
Reliability comes from design rather than hope. I give each agent only the tools it needs, record every step so behaviour can be reviewed, set limits on cost and number of actions, and build test cases from real examples so changes can be checked before they ship. No agent is perfect, so the system should fail safely and ask a person when it is unsure.
What agents can do for a business
A research agent can search several sources, compare what it finds and return a short briefing with links. A support agent can read an incoming question, look up the relevant policy or order information and prepare a reply for a person to approve. An analytics agent can pull figures from several tools and explain in plain language what changed. In each case the value is the same: the agent handles the gathering and first draft, and a person keeps the final say.
What is included in an agent project
Use-case scoping
We define the job, the success measure and what the agent must never do.
Agent architecture
A LangGraph design with clear states, branches and stopping conditions.
Tool and API integration
Connections to search, databases, stores and internal systems the agent can use.
Research and support agents
Agents that gather information, summarise it or prepare answers for review.
Evaluation and guardrails
Test cases, step logging, spending limits and human approval for risky actions.
Deployment and handover
The agent running in your environment, with documentation for your team.
How an agent project runs
- Define the job. One clear task, one success measure and a list of actions that need approval.
- Prototype. A first agent is built with the minimum tools it needs.
- Test with real cases. We run it on real examples, fix failures and tighten the guardrails.
- Deploy and monitor. The agent goes live with logging so you can see what it does.
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot mainly converses. An agent can take actions: it calls tools, looks things up, works through several steps and decides what to do next to finish a task.
Which AI models do you use?
I choose the model for each task based on quality, cost, speed and your privacy requirements, rather than tying you to a single provider.
Can an agent act without my approval?
Only where you allow it. Low-risk steps can run automatically, while actions such as publishing, sending or changing prices can wait for a human to approve.
How do you stop agents from making mistakes?
Narrow roles, limited tools, test cases, logging and approval steps all reduce mistakes. No honest developer can promise zero errors, so the system is designed to fail safely.
Have a multi-step task an agent could handle?
Tell me what the task involves and what a good result looks like, and I will advise whether an agent, a workflow or something simpler is the right tool.
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