Agentic AI Explained: Workflows vs Agents

The next wave in AI is agentic systems, where AI autonomously plan, decide, and act toward goals. In this article, we explain the two primary forms of agentic AI: AI agents and agentic workflows. Identify which strategy best suits your use case, and get a demonstration for how to build an agentic workflow using Orkes Conductor, an enterprise-grade platform for orchestrating distributed systems and AI components.

What is agentic AI?

Agentic AI refers to AI-driven systems that operate fully or semi-autonomously to achieve goals without step-by-step instructions. These systems integrate reasoning modules (often LLMs), tool interfaces, memory, and feedback loops to make decisions, adapt to context, and execute tasks in real time.

This approach represents a shift from traditional rule-based or predictive models toward goal-driven, self-directed architectures. Core characteristics include:

• Autonomy: Operate without requiring human input at every step.
• Goal Orientation: Plan and execute toward defined objectives.
• Adaptability: Adjust behavior in response to new inputs or environmental changes.
• Self-Improvement: Learn from outcomes to refine future decisions.
• Interactivity: Leverage tools, databases, systems, other AI agents, or humans as needed.

Agentic AI systems can be implemented as AI agents or agentic workflows, which offer distinct architectural patterns and benefits.

AI agents vs agentic workflows

When it comes to agentic AI, many tend to confuse AI agents with agentic workflows. While both are decision-centric systems that can act autonomously, there are some fundamental differences in their underlying architecture and, subsequently, the extent of their autonomy. In general, agents are ideal for more dynamic uses, while workflows are best for more structured scenarios. Let’s explore each approach in turn.

What are AI agents?

An AI agent is an autonomous software entity that perceives its environment, reasons about its goals, and takes actions. Examples include:

• A chatbot resolving customer queries
• A scheduling assistant managing calendar events
• A coding agent generating boilerplate code

Modern agents are often built around LLMs, configured with:

• System prompts for behavior guidance
• A toolset (e.g., APIs, search, database access)
• LLM parameters like max_turns or temperature for reasoning control

AI agents are the building blocks of an agentic AI system. Each agent encapsulates a specific capability or behavior, like booking flights or writing frontend code. Single-agent systems work well for bounded tasks, like a research agent. For complex, multi-domain challenges like website building, multi-agent systems are better for coordinating specialized agents.

Architectural components:

• LLM core: Reasoning and decision-making capabilities, powered by prompts
• Tool Wrappers: Interfaces to external APIs or systems
• Memory: Store of intermediate context or results

A popular prompting pattern is the ReAct framework, where the agent is instructed to explicitly reason through Thought → Action → Observation cycles. Here is an example prompt:

Resolve the customer query with interleaving Thought, Action, Observation
steps.
- Thought can reason about the current situation.
- Action can be three types:
(1) Search [query], which searches the internal knowledge base for information regarding the customer query and returns a targeted response if it exists.
(2) Get [customer_info], which retrieves the customer's name, email, and order history.
(3) Book [flight], which retrieves the necessary information from the customer and calls the Booking API using the information.
- Observation can interpret results to decide next steps.

What are agentic workflows?

What happens when you have to coordinate more complex processes that go beyond a single agent’s scope? This is where agentic workflows come into the picture.

An agentic workflow is a multi-step, dynamic process that orchestrates multiple API calls, AI tasks, agents, and even human-in-the-loop steps within a dynamic control graph. The workflow can branch, loop, or change course based on AI-driven evaluations, allowing it to adapt in real time.

Rather than embedding all logic inside a single agent, the workflow externalizes decision points and coordinates agents and services. Agentic workflows enable output validation, decision overriding, human oversight, and other observability features out-of-the-box. This is crucial for enterprise uses where governance over autonomous agents is needed.

Example use cases:

• Threat detection pipelines
• Fraud or claims processing
• Research assistants coordinating search, summarization, and synthesis

Key elements:

• Task Nodes: AI agents, LLM tasks, API calls, database queries, manual review steps
• Decision Nodes: AI-driven logic for routing control flow
• Working Memory: Shared state across workflow steps
• Flexible Control Flow: Branching, looping, and fallback paths for dynamic control

Essentially, the workflow provides a structure within which the AI agent can choose different paths or repeat steps as needed.

Workflow implementation:

Agentic workflows are typically built using orchestration platforms that support AI-driven development. With state management, execution tracing, retry and error handling policies, the orchestration engine can coordinate many design patterns:

• Externalize a single agent’s high-level flow for observability
• Orchestrate multiple agents to collaborate on a project
• Embed an agent as part of a wider process

With tools like Orkes Conductor, developers can design workflows visually or programmatically, embedding AI tasks seamlessly alongside microservices, databases, and human oversight.

Differences between an AI agent and an agentic workflow

In summary, an AI agent solves problems in an emergent manner: the LLM, powered by a prompt, dynamically directs tools and processes to accomplish tasks. Meanwhile, agentic workflows are orchestrated through explicit control flow paths consisting of tools, databases, AI agents, and even humans. Here’s a table summarizing the key differences between AI agents and agentic workflows:

Area	AI Agent	Agentic Workflow
System Composition	A single entity with an internal reasoning loop to execute the set of tasks it’s responsible for.	An orchestrated series of tasks across agents, services, and APIs, in a dynamic and iterative sequence.
Architecture	Opaque (black box), with minimal external control.	Modular, traceable, with externalized control flow.
Autonomy	Does not follow a strictly pre-coded sequence, with freedom to choose actions within whatever capabilities it has.	Follows defined stages at a high-level, but can dynamically choose execution paths at runtime​.
Decision-Making	Internalized within the agent’s chain-of-thought process.	Externalized to workflow decision nodes based on the LLM’s evaluation (if result X > threshold, do branch A else branch B).
Adaptability	Highly adaptive, but also highly unpredictable.	Adaptive with guardrails, fallbacks, and manual reviews.
Traceability	Low — difficult to debug or audit.	High — step-wise visibility for auditing, logs, and metrics.
Control	Custom implementation required for guardrails and agent control.	Built-in controls via orchestration, human checkpoints, and retries.

Choosing between an agent and a workflow

Deciding whether to use a standalone AI agent or an agentic workflow depends on the process’s complexity, the need for control, and the operational environment. Here are some key considerations:

1. Task complexity

   Use agents for simple, self-contained tasks, like a web search agent. For multi-stage or multi-agent pipelines, like supply chain management or financial trading, workflows offer better performance control through orchestration.

2. Governance and reliability

   Agents can be unpredictable. If you need control, validation, or safety checks, workflows offer a deterministic structure with clear checkpoints, timeouts, and human sign-offs.

3. Dynamism vs predictability

   Agents excel in dynamic environments, adapting in real time without predefined rules. Workflows require predefined decision points but can include AI-powered logic for flexible branching. If your process can be loosely modeled, workflows work well; if not, opt for agent loops.

4. Multi-agent coordination

   Complex tasks often benefit from a modular approach that leverages specialized agents rather than one monolithic agent. Workflows orchestrate these efficiently—either sequentially or in parallel—and manage integration of their outputs.

5. Transparency and troubleshooting

   Workflows are more debuggable and audit-friendly, with visual diagrams, logs, and metrics to trace decisions, failures, or delays. In contrast, agent reasoning is harder to interpret and may raise compliance concerns in regulated environments.

6. Development effort and flexibility

   Agents are quicker to prototype and ideal for early-stage or lightweight use cases. Workflows may be more demanding to design but provide long-term reliability, scalability, and maintainability.

When to use what

• AI Agents: Best for self-contained, intelligent tasks with fast set-up and minimal control needs. Ideal for prototyping or experimenting with ultra-dynamic processes.

• Agentic Workflows: Suitable for complex, multi-step, and high-reliability scenarios. They offer structure, observability, and safe integration of AI components.

One approach is to start with an agent for prototyping and migrate to a workflow as you scale into production for better governance.

Building an agentic workflow in Orkes Conductor

In this section, we’ll walk through how to build an agentic workflow using Orkes Conductor, an orchestration engine for building modern workflows and agentic systems. We’ll use a practical example of an agentic research assistant to illustrate the process.

Step 1: Set up Orkes Conductor

Sign up for the free Developer Playground to get started with Orkes Conductor.

Step 2: Design the workflow

Here’s the high-level flow for our research agent workflow:

1. Accept the user’s question as input and identify what to do next (e.g., literature review or research gap).
2. Synthesize sub-topics based on the user’s questions.
3. Use search grounding to compile research for each sub-topic.
4. Synthesize research into a clear report and return the answer.

To get started quickly:

Import the agentic research workflow template from the Launchpad in the Developer Playground left navigation panel.

You will see the agentic_research workflow.

Before you can run the workflow, you need to add the AI integrations and prompts in the next few steps.

Step 3: Integrate AI services

Next, integrate the necessary AI services. For instance, if you plan to use OpenAI’s gpt-4o for researching topics, you must add an OpenAI integration and its models to Conductor.

Using the imported agentic_research workflow, the following required AI integrations have already been imported:

• openAI–used to identify what task to do.
• perplexity—used for research generation with web search grounding.
• AnthropicClaude—used to synthesize the final report.

To integrate the AI providers:

1. In your agentic_research workflow, go to the Dependencies tab in the right-hand panel.
2. For each integration, provide the API Key and select Save.

Once integrated, add the AI models for each integration:

1. Go to the Integrations tab in the right-hand panel.
2. For each integration, select the Add/Edit models icon (+) and select New model.
3. Add the following models for the corresponding integration:
   1. openAI–gpt-4o
   2. perplexity—sonar
   3. AnthropicClaude—claude-3-7-sonnet-20250219

Step 4: Define AI prompts

Conductor has powerful features for defining reusable AI prompt templates. Use this to define the prompts for LLM models for generative, evaluative, and reasoning purposes.

For example, your research agent workflow needs to break down a research topic into relevant sub-topics, like so:

Variables like ${user-query} enable you to create flexible, reusable prompts.

Using the imported agentic_research workflow, the following required AI prompts have already been created:

• break_into_subtopics—breaks a research query into distinct subtopics.
• query_task_decision—determines subsequent tasks (research-gap, literature-review, both or none) based on the user's query.
• literature_review_task—conducts a literature review given a subtopic.
• research_gap_task—conducts a research-gap analysis given a subtopic.
• compile_subtopic_responses—compiles a report, given a list of literature reviews and/or research gap analysis.

To use the AI prompts in the workflow:

1. In your agentic_research workflow, go to the Dependencies tab in the right-hand panel.
2. Under Prompts, add the following models for the corresponding prompt:
   1. break_into_subtopics—openAI:gpt-4o
   2. query_task_decision—openAI:gpt-4o
   3. literature_review_task—perplexity:sonar
   4. research_gap_task—perplexity:sonar
   5. compile_subtopic_responses—AnthropicClaude:claude-3-7-sonnet-20250219

Step 5: Test the workflow

Once the workflow is ready:

1. Test it with a sample input question, like “what is the latest developments in liver cancer research?”
2. Inspect the execution graph and task logs in the Conductor UI.
3. Review the output.

Use this chance to debug and refine the workflow further.

Step 6: Deploy and iterate

When ready to deploy, you can expose the workflow as a service using Conductor’s Start Workflow API. You might also enhance it by implementing other orchestration patterns:

• Parallel agent execution: Use a Fork/Join operator to conduct research across multiple LLM models in parallel, then compare their answers to produce a finalized research report.
• Human-in-the-loop: Insert Human tasks at key decision points. For example, you can enable the user to modify the generated list of research sub-topics before proceeding to the next task.

Why use orchestration?

Orchestration is essential for implementing agentic systems effectively. Using Conductor, we gained:

• Observability: Execution tracing, metrics, and logs into every AI action
• Governance: Structure enforcement, human-in-the-loop approval gates
• Integration: Seamless connection to services, APIs, and agents
• Reusability: Prompts, tasks, and integrations sharing across workflows
• Reliability: Built-in retries, error handling, and scaling

Conclusion

Agentic AI enables flexible, autonomous automation. While standalone agents offer vast adaptability, agentic workflows provide the control, observability, and coordination needed for production environments. The optimal strategy combines both approaches:

• Use workflows to govern and control AI agents
• Use AI agents to inject intelligence and adaptability into workflows.

When done right, this hybrid model allows you to build powerful, flexible automation systems for use cases that were previously too brittle or impossible to automate. With orchestration tools like Orkes Conductor, teams can build robust, intelligent automation systems ready for enterprise use.

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Orkes Conductor is an enterprise-grade Unified Application Platform for process automation, API and microservices orchestration, agentic workflows, and more. Check out the full set of features, or try it yourself using our free Developer Playground.