Shared agentsArtificial Intelligence
AI runs inside business processes governed by an orchestration layer, which decides when an AI task runs, what data it sees, and where a person reviews the result.
- AI & Intelligent Platforms
//Agentic AI
Enterprise AI agents,the next evolution of automation.
Enterprise AI agents are outcome driven: from task automation to proactive decisions and goal pursuit, acting independently within clearly defined boundaries.
Outcome driven
From automation to orchestration
Agents respond to what's happening, remember past events, and adjust to reach the goal.
Business
For customer-facing teams
Agents handle forms, validation, and responses, surface next-best actions, and flag governance gaps.
Technical
For technical teams
Agents monitor system events, predict failures, and launch resolution workflows from triggers.
Agents operate inside case models that manage their goals, data, permissions, and progress.
You stay in control. The agents do the work.
//Overview
What is agentic AI in the enterprise?
Agentic AI in the enterprise uses AI agents that pursue a goal rather than follow a fixed script: they trigger on events, track their own progress, call business systems and ask for help when they are uncertain, within clearly defined boundaries.
DataObserve Flow gives agents those boundaries. Agents work inside governed processes and cases, with entitlements that define what each agent can see and do, and decisions that stay traceable, auditable and explainable.
//Trustworthy agentic AI
Everything you need to build,run, and govern agents.
Logic or events
Trigger flows on logic or events
Agents start processes when a threshold, user action, or external event occurs.
//Business performance
Maximizing business performance with agents
Boost productivity
Multi-step, context-heavy work, 24/7, with consistent logic.
Scale operations
Agents adjust in real time to workloads and policies.
Accelerate decisions
Faster, more accurate decisions, with humans in the loop.
Support innovation
Teams freed for higher-value work.
//Explore more withDataObserve
More from Flow · AI & Intelligent Platforms
Agentic Case Platform
The case is the harness: it decides what an agent may do.
- AI & Intelligent Platforms
Case Management
Automation, orchestration, and human decision-making combined, to adapt to unpredictable tasks and decisions over the life of a case.
- Case & Process Management
Process Orchestration
Encode and enforce the end-to-end process, so the business can evolve fast at minimal cost, with long-running automations for routine and exception-driven work.
- Case & Process Management
//Challenges and risks
New opportunities, new responsibilities
Trust and explainability
Decisions must be traceable, auditable, and explainable, especially in regulated work.
Data quality and governance
Agents rely on structured, current data to act accurately.
Security and access control
Entitlements define what each agent can access, see, or do.
Skills and readiness
Success takes the right architecture, oversight, and cross-team tools.
//FAQs
Agentic AI: questions teams ask
What is the difference between AI automation and agentic AI?
AI automation performs a defined task. Agentic AI decides which steps to take toward an outcome, so it needs clear boundaries, entitlements and escalation paths.
How do AI agents stay within boundaries?
Each agent works inside a process or case that defines what it may access and do, and escalates to a person when it is uncertain.
Who is agentic AI for?
For customer-facing teams that want faster outcomes, and technical teams that need to build, run and govern agents reliably.
What should an agent do when it is uncertain?
Escalate. Agents in DataObserve Flow ask a person for help when confidence is low, and the process records the handoff.
