AI practice
MCP, multi-agent systems, and the evaluation patterns that tell you whether an agent is actually doing better than a deterministic workflow.
AI agents are the most over-claimed category in the current AI market. A lot of "agentic" systems are RPA with an LLM stapled on; some are genuinely better than the deterministic workflow they replaced; a smaller number are doing things that genuinely require an agent. Our AI agents practice exists to tell the difference. We design agentic systems where they earn their keep — and recommend simpler architectures where they don't. Honest, opinionated, evaluated.
What we deliver
When an agent is the right architectural answer. Multi-step reasoning, tool use, MCP-style integrations, evaluation frameworks. Output: a designed system, scoped to a real outcome.
Model Context Protocol implementation — for teams building agentic systems that need to expose their data + tools to agents. Schema design, security, evaluation.
Beyond unit tests for prompts. Multi-step trajectory evaluation, tool-use accuracy, end-to-end success rates, regression detection. Output: confidence in what the agent is doing at scale.
For teams considering whether to migrate from RPA to agentic systems. A focused assessment that names which workflows benefit from migration, which don't, and why.
When to engage us
Stack
Engagement-specific stack choices are always driven by your constraints. The below is what we have current production experience with.
FAQ
When the workflow is well-defined and deterministic. Most "agentic" systems we see in the wild would be better as a state machine with one LLM call at a decision point. Genuine agentic patterns earn their keep when the workflow has unbounded branching, requires multi-step planning over novel inputs, or needs to use tools the system designer can't anticipate. Most workflows don't fit those criteria.
Three layers: (1) trajectory evaluations — does the agent take a reasonable path through the decision tree? (2) tool-use accuracy — when it calls a tool, does it call the right one with the right inputs? (3) end-to-end success — does the user's underlying goal get achieved? We build evals against your labelled data; without those, "the agent is working" is a vibe, not a fact.
Architectural constraints rather than prompt-level pleading. Limit the tools the agent can call; require human-in-the-loop confirmation for irreversible actions; sandbox the execution environment; log every action with audit trail. The pattern is the same as least-privilege engineering in any other context.
Yes. MCP is the most useful interop standard to emerge in 2025 for agentic systems and we build both MCP-consuming agents and MCP-server implementations. For teams exposing internal data or tools to an external agent (e.g. Claude or Cursor), getting the MCP server right matters disproportionately for the agent's usefulness.
Next step
Discovery calls are 30 minutes, no deck, no pitch. We’ll tell you honestly whether we’re the right team for your specific situation.