What are agentic workflows and how to build them for your business
Agentic AI workflows are autonomous, adaptive sequences of actions executed by AI agents that reason, plan, and act to complete complex business goals rather than following fixed, rule-based scripts.
According to IBM, these workflows leverage reasoning, planning, and tool use to execute complex tasks efficiently, adapting to real-time data and unexpected conditions.
But here's what most explanations miss: the hard part isn't the AI. It's the orchestration.
You can deploy the most sophisticated LLM-powered agents available, and they'll still produce chaos if there's no structure governing how they interact, who owns the decisions that matter, and what happens when an exception needs human judgment.
In this guide, you will learn what are agentic AI workflows and how you can build it for your business
Key takeaways
Agentic workflows differ from traditional automation at a structural level. They enable adaptive, autonomous execution that handles complexity and variation, but only when orchestration logic is explicitly designed.
Multi-agent systems are foundational, not optional. Single agents struggle with breadth and complexity. Real agentic workflows distribute tasks across specialized agents that collaborate, share context, and hand off work reliably.
Orchestration determines whether agentic systems scale. Platforms like Moxo that provide structure, control, and visibility anchor workflows in enterprise-ready governance, turning experimental AI into reliable business operations.
Human accountability must be preserved. AI handles coordination and preparation. Humans own the decisions that matter: approvals, exceptions, and risk calls.
Why agentic AI workflows matter for architects
Traditional automation handles linear, predictable processes well but business operations aren't linear. They involve exceptions, conditional branching, external dependencies, and decisions that require human judgment.
Gartner predicts 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from less than 5% today. This shift will transform enterprise applications from tools supporting individual productivity into platforms enabling seamless autonomous collaboration and dynamic workflow orchestration.
For leaders, the question isn't whether to adopt agentic approaches. It's how to design systems that preserve accountability while enabling autonomy. An agent that can "do anything" without guardrails is a liability.
Moxo's helps businesses embed AI agents inside defined processes, keeping decisions visible, auditable, and human-owned.
Multi-agent systems: The engine behind agentic workflows
At the core of advanced agentic workflows are multi-agent systems: collections of autonomous agents, each with a role or specialization that contributes to completing a larger task.
Why single agents fail at scale. A single agent handling an entire business process quickly hits limits: context window constraints, reasoning depth, and the breadth of capabilities required. Gartner reports a 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025, reflecting skyrocketing enterprise interest.
Communication is the coordination layer. Agents must share context, status, and decisions within a workflow. Without effective communication, agents work in isolation, producing inconsistency and duplicated effort.
Consider a customer onboarding workflow built on Moxo. A Data Intake Agent collects user details. A Verification Agent checks compliance and credentials. A Task Orchestrator assigns downstream actions. A Notification Agent updates stakeholders. Each agent specializes, but Moxo's orchestration layer delivers a unified outcome.
"Moxo has helped us completely streamline our project management and client communication process. It's made our workflows much more organized, our team more accountable, and our clients more informed." —-G2 reviewer
Designing agentic workflows: Core principles
Task decomposition is where most designs succeed or fail. Effective agentic workflows break down complex business goals into discrete, manageable sub-tasks that map to specialized agents. Modularity ensures scalability. In a support escalation workflow, initial triage, categorization, and routing should each be handled by different agents.
In Moxo, you model these as structured actions within a workflow: approvals, acknowledgements, file requests, tasks, forms, e-signatures. Each becomes a discrete step with clear ownership. Milestones group related actions into stages, so you track progress and surface bottlenecks before they cascade into delays.
Agent coordination requires explicit orchestration. Multi-agent systems need orchestrators to manage communication and ensure agents act toward the same business objective. This means defining how agents share context and results, establishing protocols for handoffs, and building in checkpoints where human oversight is required. A process without clear accountability isn't a process. It's a shared assumption.
Human-in-the-loop isn't a limitation. It's the architecture. The most common mistake in agentic workflow design is treating human involvement as a fallback for when AI fails. Wrong framing. Humans own the decisions that matter. AI handles everything around those decisions: preparation, validation, routing, follow-ups.
5 practical steps to build agentic AI workflows
Step 1: Define business goals first. Start with outcomes, not capabilities. What does the workflow need to achieve? Faster vendor approvals? Reduced exception handling time? Identify metrics that matter: cycle time, accuracy, throughput.
Step 2: Decompose into sub-tasks and assign agents. Break the primary goal into logical subtasks. Map each to an agent based on capability. In Moxo's visual workflow builder, you define execution sequences, branching conditions, and approval gates without custom code.
Step 3: Design communication protocols. Establish how agents share context and results. Moxo handles this through its orchestration layer. Notifications and intelligent nudges automatically alert participants when action is required. Context travels with the workflow.
Step 4: Choose your orchestration platform and AI agents. This is where most teams either succeed or spend six months building infrastructure they could have bought. Moxo provides both orchestration and embedded AI agents:
AI Review Agent validates submissions against criteria, flags issues, and routes exceptions to humans when judgment is required. AI Prepare Agent stages decisions before they reach humans, pre-filling forms and attaching relevant context. AI Chat Assistant provides in-context guidance during execution.
Step 5: Implement feedback loops and monitoring. Moxo's operational visibility surfaces where work stands, what's blocked, and what's moving. You're identifying bottlenecks early so teams intervene before cycle times slip.
The new way of scaling businesses
Agentic AI workflows represent the next generation of business process automation: systems that think, plan, and act beyond static rules. But the sophistication of the AI is only as valuable as the orchestration that governs it.
Moxo provides the process orchestration layer where human actions, AI agents, and systems work together. When AI agents operate within defined processes and humans remain accountable for critical decisions, you get efficiency without losing control.
Get started with Moxo to orchestrate agentic AI workflows reliably, securely, and at enterprise scale.
FAQs
What's the difference between agentic workflows and traditional automation?
Traditional automation follows fixed rules: if X happens, do Y. Agentic workflows use AI agents that reason through complexity, adapt to changing conditions, and coordinate across multiple steps. The difference between AI agents and agentic AI agents is a script and a system that handles exceptions intelligently.
How do I ensure accountability when AI agents are making decisions?
Design human-in-the-loop checkpoints at critical junctures. AI agents should handle preparation, validation, and routing. Final approvals on decisions with real business impact stay with humans. Moxo's orchestration layer makes it clear who owns each decision.
Where do agentic workflows create the most value?
They excel in multi-step processes with high variability: customer onboarding, compliance verification, exception handling, cross-department approvals, and any workflow where conditions change and human judgment is required at specific points.
What infrastructure do I need for multi-agent systems?
You need an orchestration layer managing agent communication, state tracking, and error handling. Production deployments also require observability tools and governance controls. Starting with a purpose-built platform like Moxo significantly reduces build time.
How do I start if my team has no experience?
Begin with a single process that has clear pain points and measurable outcomes. Map the current workflow, identify where AI can handle preparation and coordination, and define where humans must remain in the loop. Pilot with Moxo's no-code builder before building custom infrastructure.
