Agentic AI

An agent is a language model working in a loop: it reads the task, calls a tool, looks at the result and decides what to do next. Retrieval gives it your knowledge, tools let it act, and a supervisor can split longer processes between specialist agents.

What we build:

• Permission-aware RAG over SharePoint, Confluence, Google Drive, S3 and your databases, with every answer citing its source
• Narrow, well-named tools such as get_tenant_ledger or draft_arrears_notice, never raw SQL
• Read-only access by default, with write access granted one tool at a time
• Multi-agent workflows only where a single agent with good tools can't handle the job
• Built with LangGraph or the Claude Agent SDK, depending on the workflow

A demo that works on five tasks tells you very little. Before an agent ships, we build an evaluation set from the real work it will do, and it runs again every time a prompt, tool or model changes.

Included:

• Task success rate measured on replayed real scenarios
• Grading on correctness and on whether each answer is grounded in its sources
• Checks that the agent picked the right tools, in a sensible order
• Prompt-injection and PII tests on inputs and retrieved content
• Model choice (Claude, GPT, Gemini or open-weight) based on evaluation results rather than brand

An agent in production needs the same care as any other service. We deploy it where your team works, and trace every run so you can see which step went wrong and why.

What's covered:

• Delivery as a Teams or Slack bot, inside your internal tools, or as an API behind your SSO
• Deployment in your cloud or on-premises, with versioned prompts and rollback
• Step-level traces in LangSmith, MLflow Tracing or Langfuse
• Cost and latency tracked per run, so a runaway loop shows up before the invoice does
• An audit log of every task, the tools called and the result