What is an Agentic Workflow?
An agentic workflow is a sequence of tasks in which one or more AI agents interpret a goal, determine the actions required, use available tools and information, and move work toward an outcome.
Unlike a fixed automation workflow where every step follows a predefined sequence, an agentic workflow can adapt its path based on context, results, exceptions, and new information.
For example, an agent handling supplier onboarding may extract documents, identify missing information, verify requirements, request additional evidence, route an exception for human review, update a procurement system, and continue tracking compliance after onboarding.
AI agent platforms similarly describe agents as systems that can interpret user requests, break work into steps, access knowledge, interact with applications, and call APIs to perform actions.
How an Agentic Workflow works
An agentic workflow usually begins with an objective rather than a rigid list of instructions.
The system first gathers the information required to understand the task. This may include user input, documents, enterprise records, historical context, business policies, or information retrieved from connected systems.
An AI agent then determines what should happen next. Depending on the task, it may retrieve additional information, use a tool, call an API, ask another specialized agent to complete part of the work, or request human approval.
The result of each action becomes new context for the next decision.
This creates a continuous cycle:
Understand → Plan → Act → Evaluate → Continue or escalate
In enterprise environments, governance can be applied throughout this workflow. Actions can be restricted by policy, approvals can be required at specific points, and execution can be recorded for review.
elsai's platform applies this model through multi-agent workflows with observability, Guardrails, Agent-to-Agent coordination, domain intelligence, and human-in-the-loop controls.
Key capabilities
Agentic workflows are built around five core capabilities.
Goal-driven execution - Agents work toward a defined business outcome rather than simply responding to individual prompts.
Context-aware decisions - The next step can change depending on business data, previous actions, system responses, or exceptions.
Tool and system interaction - Agents can retrieve information and interact with applications, APIs, databases, and other enterprise tools.
Multi-agent coordination - Specialized agents can divide responsibilities and hand work to one another across a larger process.
Human escalation - Cases that require judgment, approval, or intervention can be routed to the appropriate person.
Why Agentic Workflows matter
Traditional automation performs well when a process is predictable and every decision can be encoded in advance.
Many enterprise processes are not that simple.
Procurement negotiations change as vendors respond. Prior authorization cases vary by payer, procedure, documentation, and patient context. Contract reviews uncover different risks. Compliance workflows produce exceptions that cannot always follow the same path.
Agentic workflows provide more flexibility because the system can evaluate the current situation before determining the next action.
For enterprises, however, flexibility must come with control. The workflow needs visibility into what agents are doing, policies that define what they can do, and clear points where human authority takes over.
elsai's operating model reflects this by combining specialized agents with governance at every handoff and full workflow visibility.
Frequently asked questions
How is an Agentic Workflow different from a traditional workflow?
Can multiple AI agents participate in one workflow?
Do Agentic Workflows operate without humans?
What types of processes are suitable for Agentic Workflows?
