One agent with every permission is hard to defend in an audit. Relevant Software builds multi-agent systems where each agent has one role, its own permissions, and a record of every action, so any decision shows who prepared it, who checked it, and who approved it.
We check whether your process needs a multi-agent system or one agent with the right tools.
Each agent gets one job and a clear place in the flow, so responsibility never blurs between them.
The system splits work the way your control framework already splits it between people, so maker-checker and four-eyes rules carry over to agents.
Agents need a shared view of the case without passing sensitive data where it doesn’t belong.
Testing each agent alone misses the failures that happen between them. We test the system as a whole and keep every decision reproducible.
Agents working together can loop, repeat actions, or run up costs. Hard limits stop that before it reaches production data.
In a regulated process, the agent that prepares a decision should never be the one that approves it. A typical flow looks like this:
Your target process, its control points, and the systems it touches go through discovery, and you leave with a map of where agents fit and where people stay.
Roles, permissions, hand-offs, and approval points reach a design your process owners, security, and compliance teams sign off.
Agents, shared state, and tool access are built against the approved design and connected to your systems in staging.
Scenario tests, stress tests, and decision replay run across the full agent chain before any live case reaches it.
The system handles real cases with people approving every decision at first, then only where policy requires.
Code, role maps, and runbooks transfer to your team, and AI agent monitoring and orchestration keeps the system in view after launch.
92% senior talent. The people who design agent roles and permissions have built production systems where mistakes cost money.
LangGraph, Microsoft Agent Framework, CrewAI, or Google ADK. We choose the framework that fits your process and stack, not the one we used last.
ISO 27001, GDPR, and HIPAA-aligned delivery. Permissions and audit trails are built to the standards your reviewers already accept.
Open Banking, HL7 FHIR, and PSD2 delivery experience, so control and audit questions get answered from projects already shipped.
96% employee retention. The engineers who design your agent roles stay on through launch.
Each change built with AI-assisted development carries a senior reviewer’s sign-off, resulting in up to 50% faster delivery.
A multi-agent system makes sense when a process has distinct stages that need different permissions, different data, or independent checks. If one agent with a few tools can do the job and no control requires separating the work, one agent is cheaper to build and easier to run, and our custom AI agents for enterprise workflows cover that case.
A quick test: list the approvals and checks your current manual process requires. If the same person isn’t allowed to do two of those steps, the agents shouldn’t be either.
We give each role in the process its own agent with its own permissions. The agent that prepares a decision can’t approve it, and the agent that checks it has read-only access. A person approves wherever your policy requires human sign-off.
The role map is written in terms your control owners already use, so it can go into your control documentation alongside the human process.
We work with all of them and choose per project. LangGraph suits flows with explicit state and branching, Microsoft Agent Framework fits teams already on Azure and .NET, and Google ADK fits Google Cloud environments. For communication between agents, we use open protocols such as MCP and A2A, so no part of the system is tied to one framework.
If your team already has a standard, we build on it rather than add a second one.
Every task runs with hard limits on steps, time, and spend, and the system detects repeated or circular hand-offs. When a limit is hit, the case goes to a person instead of continuing.
We test these limits under load before launch, because loops tend to appear only when several agents are busy at once.
Yes. Each step logs its inputs, the agent or person who acted, and the output, so a specific decision can be reconstructed from start to finish. Where needed, the system can re-run a case against the same inputs to show the result is reproducible.
Agree on retention periods with your compliance team early, because they decide how long those records must be kept.
Multi-agent systems development builds a set of agents that work together on one process. AI orchestration development builds the platform layer that routes requests across models, teams, and budgets for your whole organization.
Many enterprises need both: orchestration as shared infrastructure and multi-agent systems for specific processes. Both fit into a wider enterprise AI agent deployment. If you’re unsure where to start, contact us.
We design the roles, split the permissions, and hand your team a system where every decision has a clear owner.
AI
AI
AI
