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AI Agents

Build and scale your agentic workforce

Automate complex work with agents in your business processes—with built-in orchestration, secure context, and auditability across your ecosystem. By embedding agents directly into the workflows that run your organization, operations transform and scale while maintaining control, compliance, and transparency.

How Appian agents transform enterprise workflows

Organizations gain measurable value when autonomous agents reason, decide, act, and learn from within end-to-end processes.

Faster throughput

Reduce cycle time in high-volume, high-judgment operational work with governed reasoning, decisions, and actions. 

Consistent quality

Apply standards and policy the same way every time. Institutionalize best judgment so performance doesn’t depend on scarce experts or individual variability.

Scaled capacity

Extend the reach of expert judgment. Agents handle complex-heavy work across high-volume processes—so growth isn't gated by specialist availability.


See AI agents in action

Appian AI agents are ready for enterprise-use.


Process is the control plane for scaling enterprise agents

Process defines what part of a job an agent is doing, what tools it can use, when it acts, and how its work fits into the broader flow of work. Agents automate complex, multi-step workflows with reasoning and autonomous action—but that value is only realized when they're integrated into the processes that govern how work gets done. This is Appian's agent approach.

Orchestration through process

Agents are embedded into process models and execute within the flow of work, moving across systems, people, and decisions — with actions and escalations managed in the same workflow.

Secure context store

Agents access secure, unified business context through data fabric. Model Context Protocol (MCP) enables seamless integration and orchestration for both Appian and third-party agents.

Transparency and auditability

Every agent execution is monitored, audited, and evaluated, giving teams the observability and accountability needed to scale.

Use cases for AI agents across enterprise workflows

Explore some of the core use cases where autonomous agents are multiplying workforce capacity and eliminating operational bottlenecks.

 

Access context across systems to correlate signals and surface insights or action

Interpret incoming requests and unstructured inputs and determine and action next steps within the workflow

Cross-reference transactions against evolving regulatory policies, instantly flagging anomalies, executing checks, and ensuring audit-readiness

Engage customers and reference data and policies across systems to resolve inquiries

Evaluate context, triage disputes, and dynamically route complex claims to the right human expert only when nuanced judgment is required

One platform, endless possibilities

The Appian Platform connects agents to your data, systems, and other automation tools. With one unified platform for your enterprise automation needs, the possibilities are endless.

Orchestrate your agentic workforce

Build, monitor, and deploy AI agents for repeatable enterprise use.

Join other companies who are using AI to automate real enterprise work

Patient intake

Acclaim Autism deployed AI agents in three weeks to cut patient intake time by 83%.

Accounts payable

Century Fire Protection automated accounts payable with AI, reducing invoice processing time by 36%.

AI chatbot

USF uses AI to save advisors 15 minutes of administrative work for every 30-minute student meeting.

Learn more about AI agents

Datasheet

Appian AI Agents

Datasheet

AI Agent Governance and Security

eBook

How to Select High-Value AI Agent Use Cases

eBook

How to Choose the Right Agentic Orchestration Model

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AI Agents FAQs

AI agents in Appian are intelligent, goal-oriented participants embedded directly into enterprise workflows. They reason, take action, and influence outcomes as part of how work gets done, rather than operating independently or autonomously.

Unlike standalone AI agents, Appian agents participate in long-running, cross-functional processes that maintain shared state, coordinate decisions across systems, and preserve accountability across teams and time. This allows organizations to apply AI safely to real operational work ,not just isolated tasks.

Many autonomous AI agent approaches struggle in enterprise environments because they lack shared context, consistent governance, and predictable execution. As agents scale, oversight increases, exceptions multiply, and productivity gains erode.

Appian takes a bounded autonomy approach. AI agents are empowered to reason and act—but only within explicit process guardrails that define when autonomy is appropriate, how decisions affect downstream work, and where escalation or human review is required. This enables applied autonomy without sacrificing control or trust.

Appian AI agents operate directly within business processes, functioning as intelligent decision or action nodes inside workflows. Processes provide shared state, sequencing, exception handling, and coordination between agents, automation, and humans.

Agents reason over business objects exposed through Appian’s Data Fabric, which provides a unified, governed view of enterprise data across systems (without data migration). Agents inherit the same permissions, security rules, and auditability as human users, ensuring context-aware reasoning with enterprise trust.

Security and governance are enforced by design in Appian. AI agents operate under the same enterprise controls that govern applications, processes, and data, including role-based access control, environment isolation, lifecycle management, and execution logging.

Every agent action is traceable and auditable, with full visibility into inputs, outputs, and process context. Governance is proactive rather than reactive (embedded into how agents are designed, deployed, and executed) supporting regulatory, legal, and internal compliance requirements.

Appian supports both human-in-the-loop and human-on-the-loop oversight models. AI agents can request approvals, route decisions for validation, or escalate exceptions based on defined rules and risk thresholds.

Humans remain accountable by design, with clear authority over when agent decisions are accepted, overridden, or reviewed – ensuring AI augments human judgment rather than replacing it.

AI agents follow Appian’s standard enterprise application lifecycle – moving from development to testing to production with appropriate controls. Built-in testing, monitoring, and troubleshooting tools support continuous improvement as agents scale across processes.

Because agents operate inside measurable workflows, organizations can directly track impact on cycle time, throughput, cost reduction, and decision quality – turning AI from experimentation into sustained operational value.

Be part of the process

Join the world’s leading organizations using Appian process orchestration.