Before deploying AI agents, check whether your processes can support them.
Robinswood diagnoses the compatibility of your processes with agentic AI: real flows, decisions, data, exceptions and safeguards required before any autonomous automation.
- mapping of processes, decisions, data and exceptions;
- agentic AI compatibility score by flow: possible autonomy, required supervision or no-go;
- data, control, role and agentic governance prerequisites;
30 minutes
A first conversation is enough to qualify the scope.
In 30 minutes, we check whether an agentic AI compatibility diagnosis is relevant or whether your priority is data, organization, control or governance.
Fast qualification
- Human reply within 24–48 business hours
- No tool selling before diagnosis
- Short form, enough context
Flow Review · qualified entry
Receive a hypothesis on your critical flow
Describe the handoff that concentrates waiting, rework or approval. We return with a first reading and decide whether a 45-minute Flow Review is relevant.
Qualify the flow, cost and mandate
This avoids a generic call and confirms Robinswood ICP and budget fit.
Signals that a compatibility diagnosis is useful
- AI agents are being considered while processes remain implicit or unstable;
- POCs accumulate without measurable return or industrialization conditions;
- useful data, business rules and exceptions are scattered across tools and files;
- teams fear autonomous automation will add complexity;
- leadership wants to invest without delegating decisions to AI on an uncontrolled flow.
Expected deliverables
- mapping of processes, decisions, data and exceptions;
- agentic AI compatibility score by flow: possible autonomy, required supervision or no-go;
- data, control, role and agentic governance prerequisites;
- 30 to 90 day experimentation sequence, from supervised copilot to bounded agent.
Best fit
- SME and mid-market leaders before budget arbitration on AI agents;
- business teams whose processes are too implicit to automate safely;
- IT or operations leaders who need to decide where AI autonomy is acceptable.
Preuve terrain
Process compatibility before autonomous agents
In a B2B services SME, the audit showed the main blockage was not AI content generation, but centralized and poorly instrumented commercial validation. The first gain came from a reliable decision flow, then from tightly bounded AI assistants and agents.