Services
Agentic AI and multi-agent systems
Automate complex workflows without treating unrestricted autonomy as a design objective.
We design coordinated AI workflows that can plan tasks, use approved tools, delegate work, exchange structured results, and respond to failures within defined boundaries.
Adding more agents does not automatically improve a solution. Multi-agent architecture is most valuable when the problem genuinely benefits from decomposition, independent responsibilities, or specialized capabilities.
What you get
- Agent orchestration and workflow state management
- Multi-agent coordination and task ownership
- Model Context Protocol (MCP) tool integration
- Agent2Agent (A2A) interoperability where appropriate
- Identity, authorization, and policy enforcement
- Execution tracing, audit evidence, and human approvals
Where it helps
Give each task a clear owner
Let an agent call only approved tools
Connect agents where interoperability helps
Require approval before a sensitive action
Technologies
- Workflow orchestration
- Tool calling
- MCP
- A2A
- Durable execution
How an engagement runs
01
Ask if agents are warranted
A single model, a retrieval pipeline, or a deterministic workflow may be sufficient.
02
Separate reasoning from authority
The agent can recommend. The platform authorizes, validates, and records the action.
03
Bound the tools
Identity, delegated permissions, policy, and a human approval for operations that need one.
04
Trace what ran
Connect the request to tool calls, policy outcomes, state changes, and the final result.
Questions
- Do we need a multi-agent system?
- Not necessarily. A single model, a retrieval pipeline, or a deterministic workflow may be sufficient. Multi-agent architecture is worth considering when distinct responsibilities, independent execution, or meaningful task decomposition justify the additional coordination and operational complexity.
- Who is the agent acting for?
- The architecture should establish the identity of the initiating user, service, or agent, and the authority delegated to it. The language model should not be the only place that question is answered.
- Is a prompt enough to stop a prohibited action?
- No. The enforcement layer must be authoritative. A prompt instructing an agent not to perform a prohibited operation is not a substitute for a technical control that prevents the operation.
- How do you approach AI security and governance?
- Security should be designed into identity, data access, tool permissions, execution controls, evaluation, and monitoring. Sensitive operations may require additional verification or human approval. The controls should reflect the use case, threat model, and applicable obligations.