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What they are, how they work and how they could change your life.
Aug 01, 2025
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In technical terms, an agentic AI workflow is a self-directed, multi-agent system where AI entities collaborate dynamically to perceive context, make decisions and execute complex tasks autonomously, adapting in real-time without requiring human intervention.
In layman’s terms, here's another metaphor to explain how an agentic AI workflow would fix a real-world problem.
Think about a rental car dispute.
Let’s say you return your rental car, having filled the tank before drop-off. As you drive out of the lot, you get a notification from your banking app and see an extra $50 fuel charge.
With traditional automation, you’re stuck in a frustrating loop. A chatbot tells you to call support. A rep needs to verify your claim. The back-and-forth takes hours, and maybe—if you persist—you’ll eventually get a refund.
Generative AI makes this process marginally easier by instantly providing the right contact information through chat. AI agents take it a step further by submitting the support ticket for you.
However, an agentic AI workflow would not only get you in touch with customer service, but refund you automatically.
How? Through multiple AI agents integrated with disparate systems and data:
This is the core of agentic AI: interconnected agents executing decisions autonomously, based on real-time data, without human bottlenecks.
Agentic AI workflows are more than just automation. They function as multi-agent systems , where individual AI entities operate independently yet collaborate dynamically. But what actual technology is required to make these connections?
Here’s another brain/body metaphor that breaks down the key components: The agents, the integration layer, the data repositories and decision engines and the security and compliance modules.
Each agent is an autonomous decision-making entity, specializing in a particular function. These could include:
Each agent can take action on your behalf, but they need to be integrated with the right systems to do so.
There are a variety of existing enterprise systems involved in the typical workflow, including:
AI agents need security, compliance and ethics hard-coded into their workflow.
Learn about Publicis Sapient’s agentic AI workflow for software development, Sapient Slingshot
While no agentic AI workflow is easy to implement, the most frequently discussed “low-hanging fruit” for agentic AI workflows is customer service. We can all unanimously agree that customer support is broken for customers, employees and companies. If businesses can successfully scale agentic AI at the right cost and with the right balance of human oversight, it will shift from an expense to a strategic advantage and even a growth driver.
But what about the workflows that aren’t so obvious to the customer, the ones that are internal? These behind-the-scenes workflows are not only ripe for disruption, but also don’t rely on consumer trust in AI overall.
Think about the B2B sales process—overburdened sales reps juggle hundreds of cold calls every day, frantically looking for their customer data across five different systems, trying to keep messy CRM systems up to date and trying to remember to check in on their endless digital rolodex of current clients.
Here’s where the agentic AI workflow comes in:
This system enables end-to-end automation by:
Each step builds on the last to help you act on the right opportunities, faster.
Function:
Aggregates relevant business data from external sources (news, financial reports, press releases) and internal updates (product changes, new offerings).
Key AI components:
Integration flow:
Function:
Monitors target client activity, tracking engagement signals across emails, meetings and CRM interactions.
Key AI components:
Integration flow:
Function:
Analyzes client intent and triggers alerts when an opportunity arises.
Key AI components:
Integration flow:
Function:
Autonomously drafts personalized sales outreach emails and schedules meetings.
Key AI Components:
Integration Flow:
Aside from the particular agents required for this sales workflow, there are also four key functionalities that this agentic AI workflow requires: enterprise integrations, infrastructure considerations, AI orchestration and data privacy and compliance.
These integrations provide comprehensive real-time data access, enable automated workflows across systems, and maximize ROI by extending AI capabilities throughout existing enterprise infrastructure.
These are the technical foundation and architecture required for deploying agentic AI systems, consisting of cloud-native environments on major providers (AWS, Azure, GCP) with serverless functions, event-driven integration for real-time data streaming, centralized data warehousing and graph databases for relationship mapping.
This is the coordination and deployment systems for managing multiple AI agents, utilizing specialized frameworks for workflow orchestration alongside containerization technologies for scalable deployment. This approach enables efficient management of complex multi-agent workflows and ensures reliable, scalable performance across varying workloads.
The regulatory frameworks and technical controls that govern how AI agents access, process and store sensitive information, including PII anonymization protocols, robust access control mechanisms and comprehensive audit logging systems. These measures ensure AI operations maintain legal compliance, protect user privacy and provide transparent accountability for all automated actions across the enterprise.
Similar to how B2B sales progresses through defined stages of prospecting, qualifying, demonstrating value and closing deals, this workflow approach can be applied to many other functional processes, including:
Each leverages the same principles of relationship development, stakeholder management and stage-based progression toward a defined outcome.
Clearly, agentic AI workflows are complex to implement but deliver quantifiable value: reducing manual processing time by 70-85 percent, operating 24/7 with over 90 percent accuracy, and effectively handling work that would require three to five full-time employees (according to internal Publicis Sapient research).
This automation ultimately frees sales professionals to focus on what humans do best: building relationships, applying strategic creativity and delivering the emotional intelligence that drives true customer success.
But before taking a deep dive into the mechanics of the workflow, technical leaders should ask themselves a few questions to evaluate how “ready” their organizations, including people, technology and processes, are to invest in an agentic ai workflow.
"
If you come up with an idea for an AI agent and begin building it without any plan for integration, you’re going to face vast infrastructure hurdles, and might just end up right back where you started.
"— Andy Maskin
Director, AI Creative Technology, Publicis Sapient
Even if your answer to every question above is clear and confident, the truth is: Most companies will need outside experts in order to begin their agentic AI journey. While the future of agentic AI promises millions of dollars saved, higher customer satisfaction scores, new business and more, the journey is more efficient when you have experts on your side.
Here’s a rough outline of what an implementation roadmap would look like, using the example of our “proactive salesperson” agentic AI workflow.
Before implementation, a deep evaluation of the current IT infrastructure is necessary to identify integration points and security risks. This includes:
By the end of this phase, enterprises will have a technical blueprint outlining the integration strategy and AI workflow design.
Instead of immediately deploying a full-scale AI system, a proof of concept (PoC) should be tested in a controlled environment with limited users and data sets.
In this phase, two key AI agents can be introduced:
To support these agents, the technical team must:
The success of this phase depends on validating whether AI-driven research and CRM insights can meaningfully augment human decision-making in sales workflows.
Once the PoC is validated, the AI workflow can be expanded to automate full sales execution.
This phase introduces two more AI agents:
To make these agents operational at scale, CIOs must:
At this stage, the enterprise will have a fully operational AI-powered sales workflow that minimizes manual tasks and accelerates deal cycles.
Even after deployment, AI workflows require continuous optimization and scaling.
Ongoing efforts should include:
The human brain is built for efficiency. It does not waste energy on redundant processes or slow down because of bottlenecks. It adapts, learns and automates where necessary, keeping everything running without conscious effort.
In the same way, businesses can evolve beyond fragmented, manual processes and create systems that move instinctively. The future of agentic AI workflows is not just about efficiency but about freeing leaders and employees from the burden of operational complexity, allowing us to execute at the speed of their intelligence.
For CEOs, these workflows will surface the most critical insights and decisions, cutting through the noise so they can focus on strategy and innovation.
CIOs will no longer be consumed by infrastructure management, as AI dynamically scales systems and corrects inefficiencies in real time.
Sales leaders will know exactly when to engage a client before competitors do, while finance teams will anticipate market shifts before they happen.
Employees will spend less time navigating administrative bottlenecks and more time on creative, high-impact work that drives real value.
These workflows will not only accelerate business performance but will also empower us to focus on what truly matters, shaping a future where work is smarter, decisions are clearer and our human potential is fully realized.
