Agents that run the work.
Not a chatbot on a page. We design systems of AI agents that perceive, decide, and act toward a goal, then collaborate with each other over MCP to run whole enterprise workflows on their own.
Multi-agent
One orchestrator. Many specialists.
An orchestrator receives the request and coordinates. Specialist agents retrieve context, reason over it, act, and check the result, handing work to each other until the outcome is done and reported. Tools and data arrive over MCP. Watch a single request move through the mesh.
01 · Receives & frames
Intake Agent
Takes the incoming request, clarifies intent, and frames the goal for the rest of the mesh.
02 · Gathers context
Retrieval Agent
Pulls grounded context from MCP-connected data sources, documents, and prior state the task needs.
03 · Plans & decides
Reasoning Agent
Weighs the options, plans the approach, and decides the next best action toward the goal.
04 · Executes
Action Agent
Calls MCP-connected tools and systems that get the work done, then reports what changed.
05 · Verifies
Quality Agent
Checks the result against the goal and policy, catching errors before they move downstream.
06 · Delivers
Reporting Agent
Packages the outcome and streams structured insight back to the people and systems that need it.
How we think about agents
Agents, not features
An agent perceives, decides, and acts toward a goal, then reports back. We build systems of them, not chatbots bolted onto a form.
Multi-agent by design
Specialist agents collaborate under an orchestrator: they delegate, negotiate, and hand off work so whole workflows run themselves.
Connected over MCP
Agents reach tools, data, and systems through Model Context Protocol, so context stays portable and integrations stay governed.