Autonomous AI Agents
Agents that plan, use tools, and complete multi-step tasks without supervision.
AI agents go beyond single LLM calls — they plan, call tools, evaluate results, and iterate. I design and build production-grade agents using LangChain, LlamaIndex, or custom orchestration layers, with the reliability engineering needed to make them safe to deploy.
Use Cases
Research and summarisation agents
Given a topic or company name, the agent searches the web, reads pages, extracts key facts, and returns a structured briefing document.
Customer service agents
Handles tier-1 support: classifies intent, retrieves from knowledge base, drafts response, escalates to human when confidence is low.
Data analysis agents
Given a CSV or database access, the agent writes and runs SQL/Python, interprets results, and answers natural-language questions about your data.
Workflow orchestration agents
Agents that manage multi-step business processes: reading inboxes, creating tasks, calling APIs, and following up — on a schedule or triggered by events.
Common Questions
How do you prevent agents from doing something wrong?
Confidence thresholds before irreversible actions, explicit human approval gates, and rollback paths for everything destructive. I don't deploy agents without undo logic.
What frameworks do you use?
LangChain and LlamaIndex for complex multi-step agents. For simpler tool-use scenarios, I often use the OpenAI function calling API directly — less overhead.
Can agents access my internal systems?
Yes — I build custom tools that wrap your internal APIs, databases, or file systems, with authentication and access scoping so the agent only touches what it should.
What you get
- →Agent architecture design (ReAct, plan-and-execute, multi-agent)
- →Tool and function definition with schema validation
- →Memory and context management (short-term + long-term)
- →Human-in-the-loop escalation paths
- →Observability: trace logging, token usage, action audit trail
- →Rollback and undo logic for destructive actions
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