Autonomous multi-agent systems that plan, use tools through the Model Context Protocol and complete multi-step workflows — with human approval gates where it matters.
Orchestrated agents that reason, retrieve, act through tools and stay accountable to your team.
A supervisor agent plans the work, routes tasks to specialized agents and keeps the whole run on track.
Agents connect to databases, APIs, files and the web through the Model Context Protocol — one standard, every tool.
RAG-grounded, memory-aware agents that act on your actual data instead of guessing.
End-to-end automation of multi-step processes that today span several systems and many hands.
Approval gates for consequential actions — agents propose, your people decide, every decision is logged.
Evaluation, tracing and observability for agents, so you can see what they did and why.
An orchestrator plans and routes work across specialized agents that retrieve context, call tools via MCP and self-verify before anything ships.
The orchestrator interprets the request, breaks it into steps and assigns each step to the right specialized agent.
Agents retrieve the documents, records and prior decisions relevant to the task, so actions are based on facts.
Agents call real systems — databases, APIs, files and the web — through the Model Context Protocol, with scoped permissions.
A review loop checks each result against guardrails; consequential actions wait for a human, and everything is auditable.
Multi-agent orchestration with planning, RAG grounding, MCP tool execution and a review feedback loop.
A fixed-scope path from one mapped workflow to agents running it in production.
We map one high-volume workflow end to end — systems, decisions, exceptions — and define the approval boundaries.
A multi-agent pilot runs real cases with MCP tool access, measured against an agreed autonomy and accuracy bar.
Deployed with guardrails, approval gates and AgentOps dashboards — handed over to your team to operate.
Full case study below — including how intake, retrieval, decision, action and review agents divide the work.
A high-volume, multi-step approval process spanned several systems and consumed large amounts of manual effort.
Designed a multi-agent system — intake, retrieval, decision, action and review — with tool and API calling and human approval gates for exceptions.
Most cases now complete autonomously, with humans reviewing only the edge cases.
Analysts spent days gathering, cross-referencing and summarizing information from many internal and external sources.
Built an agent that connects to data sources and tools via MCP, plans multi-step research and produces cited, structured briefs.
Research that took days now takes minutes, with every claim traceable to its source.
Tell us about one workflow that eats your team's time — we'll return an agent design and autonomy plan in days.
