How we talk about AI-native operations

A governed business process carried out by one or more AI agents. Missions have clear goals, rules, escalation paths, and success criteria — all defined by the business leader who designs them.

The visual environment where business leaders design, configure, and launch missions. No coding required — you define what the agents should do, what rules they follow, and when to involve a human.

An AI entity that carries out missions on behalf of your organization. Agents operate within governance-defined boundaries, earn trust through performance, and produce a full audit trail of every decision.

The pricing unit for agents deployed on the platform. One Agent Unit represents one governed AI agent. Your costs grow directly with the value you receive.

Anything an agent can connect to in order to do its work — databases, APIs, file systems, SaaS tools, or custom integrations. Resources are governed: agents can only access what their mission explicitly allows.

Organizational policies, compliance rules, and decision boundaries expressed as machine-readable, version-controlled rules. Instead of governance living in documents people forget, it runs automatically every time an agent acts.

The practice of defining missions programmatically — goals, constraints, escalation rules, and success criteria in code. This makes agent behavior auditable, reproducible, and version-controlled.

A system where agents earn or lose autonomy based on real-time performance. New agents start supervised. As they demonstrate reliable behavior, they earn more autonomy. If performance drops, oversight increases automatically.

One of four levels of agent autonomy. Restricted agents need human approval for every action. Limited agents handle routine tasks but escalate edge cases. Standard agents operate independently within defined bounds. Elevated agents handle complex decisions with minimal oversight.

When an agent encounters a decision outside its authority — too complex, too sensitive, or too novel — it routes the decision to a human reviewer with full context. The human decides, and the agent learns.

A complete, tamper-proof record of every agent decision, action, and reasoning. You can trace any outcome back to the rule that allowed it, the data that informed it, and the mission that requested it.

Every agent action is checked against governance rules before execution. If a rule says "never share customer data with a third party," the platform enforces it — every time, without exception.

Your data plane runs in your environment — any cloud, on-premise, or hybrid. Your data and organizational intelligence never leave your infrastructure. This is how the platform works, not an optional add-on.

Agion works with any AI model provider — OpenAI, Anthropic, Mistral, open-source models, or your own fine-tuned models. Switch providers without changing your governance rules or mission definitions.

The accumulated knowledge your organization builds through every mission, every agent interaction, and every decision. This intelligence compounds over time and stays in your environment — it is your competitive advantage, not ours.

The platform separates the management layer (control plane, managed by Agion) from the execution layer (data plane, hosted in your environment). You get platform updates without exposing your data.

The European Union regulation on artificial intelligence (2024/1689). Classifies AI systems by risk level and requires transparency, human oversight, and accountability. Agion is designed to meet these requirements by default.

The EU regulation governing personal data protection. Agion supports GDPR compliance through data sovereignty, audit trails, and the ability for individuals to request explanations of AI-assisted decisions.

An organization or team where AI is not a tool bolted on to existing processes, but the foundation of how work gets done. AI-native teams design processes around AI capabilities from the start, rather than automating legacy workflows.

The practice of running business processes through governed AI agents rather than manual workflows or simple automation. Agents make decisions, take actions, and learn — within explicit governance boundaries.