AI Agents Builder
A convincing demo is easy. I scope the use case, connect the tools and data sources, and ship custom AI agents that keep working once real users and real data arrive.
What's included
- Use case scoping and success criteria
- Connecting agents to tools, APIs and data sources
- Retrieval-augmented generation (RAG), so agents answer from your own documents and data
- Python for custom logic, data handling and API work
- Prompt and instruction design with clear boundaries
- Human-in-the-loop approvals for risky actions
- Logging, evaluation and monitoring
- Integration with Salesforce and automation platforms
How I work
Scope the job
We define what the agent does, what it must never do, and how success will be measured.
Wire up tools and data
The agent is connected to the systems it needs through controlled, logged actions.
Evaluate and ship
Representative test cases are used to check behaviour before launch, with monitoring afterwards.
When this is the right fit
- You have a repetitive knowledge task that follows clear rules
- A prototype agent works in a notebook but not in your systems
- You need an agent to act in Salesforce or other business tools
- You want a human approval step on higher-risk actions
Common questions.
What makes an AI agent production-ready?
Clear scope, controlled access to tools and data, human approval for risky actions, logging, and tests built from realistic cases. Without those it remains a demo.
Do I have to use Salesforce?
No. Agents can work with other business tools through APIs and automation platforms; Salesforce is one of the systems I connect to.
Let's talk about your project.
Tell me what you are trying to fix or build. I respond within one business day.