Agent Development

Safety is our primary design principle. We implement several layers of control: restricting the tools the agent can use, setting clear constraints and objectives, building in human-in-the-loop approval steps for critical actions (like sending an email or spending money), and extensive testing in a sandboxed environment.

A tool is anything the agent can interact with via an API. This can include your company's internal software (like your CRM or ERP), public web APIs (like for weather or stock data), the ability to browse the web, or access to a private knowledge base to look up information.

Our process is iterative and focused on safety:• Goal & Tool Definition: We work with you to define a clear, specific goal and the tools the agent can use.• Prototype Development: We build a prototype in a controlled environment to test its reasoning and planning abilities.• Safety & Constraint Implementation: We build in the necessary guardrails and approval steps.• Supervised Testing: We test the agent on a wide range of tasks with a human overseeing its actions.• Phased Deployment: We gradually deploy the agent to handle real-world tasks.

We use a technique called Retrieval-Augmented Generation (RAG). We convert your private documentation or data into a specialized, searchable format (a "vector database"). The agent can then be given a "tool" to securely search this knowledge base to find the information it needs to complete a task, without the underlying AI model ever being trained on your private data.

We use a cutting-edge technology stack. This includes a powerful Large Language Model (LLM) like GPT-4 or an open-source alternative as the reasoning engine, agentic frameworks like LangChain or Microsoft's AutoGen to structure the logic, and vector databases for memory and knowledge retrieval.

The main risk is unexpected behavior or "hallucinations" from the core LLM. We mitigate this through rigorous testing, clear prompting, and implementing critical human-in-the-loop approval workflows. The technology is powerful but requires a carefully managed and phased approach to deployment.

Developing an AI agent is a strategic R&D project. A Proof of Concept (PoC) to validate a specific use case is the best starting point and typically takes 2 to 4 months. This allows us to demonstrate the value and test the feasibility before committing to a full production build.

The ROI is measured by the business value it creates. This is often very clear: the number of employee hours saved on a specific task, the increase in lead generation or sales throughput, or the reduction in operational errors. We work with you to define these metrics before the project begins.