The operating layer for enterprise agents.
One governed system where specialist agents gather context, reason together, take action, verify the result, and deliver the outcome across the tools your enterprise already runs.
- Multi-agent orchestration
- MCP-connected
- Human-governed
The agent loop
From signal to governed action, in one system.
Replay a run the platform has actually done. Every hop names the specialist that acted and the system it reached, and the step that changes a system of record stops until you release it yourself.
Campaign D-114 finished overnight. Tell me what an engineer needs to look at.
Ingest · Intake Agent
Frames the request as a review pass, not a data dump
Reads the campaign as the unit of work: every sensor channel in D-114 needs a verdict, and only the exceptions need a person.
Pick a step to read it
The capability layer
The same atoms under every accelerator.
Runs like these are assembled from one shared capability layer, so the next workflow reuses connectors, controls, and intelligence already in place rather than starting another integration.
Ingest
Ground every request in what is actually happening.
- Signal Ingestion
- System Connectors
- Document Intelligence
- Vision Perception
- Voice & Telephony
Reason
Turn context into a plan that respects real constraints.
- Forecasting
- Constraint Solving
- Anomaly Detection
- Root-Cause Reasoning
- Natural-Language Query
- Risk Scoring
- Generative Design
Act
Execute through the systems where the work already lives.
- Workflow Orchestration
- Autonomous Execution
- Scenario Simulation
- Alert Routing
Govern
Keep people, policy, and evidence around every consequential action.
- Human Approval
- Evidence & Audit Trail
- Parity Validation
- Policy & Compliance
- In-Boundary Deployment
Specialist agents
One orchestrator. The right specialist for every step.
Each agent has a bounded job. The orchestrator keeps the goal, delegates the work, and carries verified context from one specialist to the next.
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.
Enterprise control
Autonomous where it should be. Accountable everywhere.
Agents run inside the customer boundary, pause at explicit approval points, and leave evidence for every input, decision, and action.
Human approval
Consequential actions wait for the right person.
Evidence trail
Inputs and decisions remain reviewable.
In-boundary
Cloud, network, or on-premise deployment.
Result checked against goal, policy, and evidence.
Platform principles
01
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.
02
Multi-agent by design
Specialist agents collaborate under an orchestrator: they delegate, negotiate, and hand off work so whole workflows run themselves.
03
Connected over MCP
Agents reach tools, data, and systems through Model Context Protocol, so context stays portable and integrations stay governed.
Start with the work