A workflow can make or break your productivity. And yet, even with the best intentions, project management inevitably falls apart. Aligning multiple roles, timelines, projects, and tasks proves to be a bigger challenge than moving a mountain.
But we don’t live in the old days anymore. Today, we have AI at our disposal—ushering in innovations like agentic workflows that are reshaping how work gets done.
Agentic workflows are built to minimize human intervention. Solving problems, making decisions, and performing tasks are all the things they’re capable of doing.
Emerging research shows that businesses using AI-driven automation have significantly reduced manual workloads. Agentic workflows build on this by enabling even deeper autonomy. And if you’re looking for more advantages of agentic workflows, keep reading.
What are agentic workflows?
In the past, employees had to manually handle approvals, tasks, and reports. But this often led to fatigue, miscommunication, and inconsistent data handling.
Now, imagine if AI didn’t just automate those tasks, but could also understand the broader goal, plan the necessary steps, and carry them out independently.
That’s what agentic workflows do.
They go beyond automation by reasoning through problems, making decisions in context, and chaining actions together to achieve an objective—freeing humans to focus on strategy, creativity, and growth.
While there are concerns that AI may replace human roles, agentic workflows are designed to work alongside people—augmenting human decisions, not replacing them.
And just like human workers, agentic systems can learn from the feedback given over time—especially when combined with retraining or reinforcement learning—making them more responsive and reliable in dynamic environments. The ultimate goal, after all, is to operate AI programs that require the least amount of intervention.
Agentic workflows bring agility to enterprise-level project management. They help validate and improve processes with speed and scalability.
Key components of agentic workflows
If you’re wondering what makes agentic workflows great, the following elements work together:
- Artificial Intelligence (AI). Of course, workflows won’t be ‘agentic’ without a touch of AI. Agentic workflows should be able to independently carry out tasks on behalf of you or another program.
- Large Language Models (LLMS). Agentic workflows can ‘communicate’, and that’s where LLMs come in. These frameworks lay the foundation for understanding human-like language, reasoning, and interacting naturally with users or other systems.
- Prompt Engineering. You’ve probably tried out ChatGPT before, and AI results will only be as good as the prompt you enter. Prompt engineering refers to the structured process of designing inputs that guide how LLMs generate outputs.
- Tools. Agentic workflows can extend their capabilities to multiple fields by accessing domain-specific information. These include APIs, databases, web search engines, or even your existing software - be it a CRM or an ERP. From financial forecasting to logistics optimization, agentic workflows can tackle all.
- Feedback Mechanisms. Just like human beings, AI needs constant monitoring and feedback to improve. The human-in-the-loop (HITL) approach particularly works best, as it empowers experts to guide, review, and correct agent decisions - even in high-stakes domains like finance.
- Multiagent Collaboration. AI can solve sophisticated problems, but not without the help of multiagent systems (MASs). Imagine a whole team with specialized agents collaborating to achieve a shared objective. That’s pretty much what an MAS is. Each of these agents specializes in a different aspect, allowing for a fair division of labor and knowledge.
- Integrations. Agentic workflows will not interfere with your existing infrastructures; they’re built to integrate your current processes seamlessly. They consolidate disparate data sources, context-specific enterprise systems like ERP, and transitional production-grade agentic systems in a unified workspace.
Benefits of agentic workflows
As you can infer from above, agentic workflows can swiftly multitask. You can delegate them to administrative duties, operational processes, financial analysis - the list goes on.
And if there’s one domain that your team doesn’t master, agentic workflows can fill the gap.
In a sequential order, all of these lead to compounded benefits. On the emotional side, employees get higher work satisfaction. At the same time, there’s an impact on your cash flow, as you don’t need to overhire for some roles, like data analysis and customer support.
I interviewed a couple of teams, asking how agentic workflows have changed the game for them. They highlighted the following benefits:
- Increased efficiency. First of all, AI agents break down large tasks into manageable chunks, focusing on one subtask at a time for efficiency. It also automates repetitive processes without sacrificing quality, handling larger workloads with fewer resources.
- Reduced errors. Errors can become costly mistakes. Thankfully, agentic workflows significantly reduce errors. Compared to human-centric processes, AI can consistently execute tasks while achieving higher accuracy over time. The more you train it, the better it becomes.
- Enhanced decision-making. AI can spot patterns that often go unnoticed by a human's eye. These unique findings help set up the trajectory of your business. Plus, AI can structure, visualize, and organize those findings into actionable and digestible charts and reports.
- Scalability. AI can handle increasing data volumes, workloads, and user demands without quality degradation. They can also adapt to changing conditions, evolving business objectives, and newer technologies without disrupting your day-to-day operations.
- Cost savings. After using AI, companies report an average cost reduction of $4,739/month. The main reason is that AI significantly optimizes resource allocation. As a result, you can use the available budget for growth initiatives.
How do agentic workflows work
Previously, AI could only do the most basic tasks, like password resets. But now, with agentic workflows, AI can solve complex and multi-step problems.
For instance, your team receives a support ticket notifying of a bug in a new product launch. The old version of AI wouldn’t know what to do. But now, it will be able to assign the right people to the right job, send a Slack notification, or any other messaging channel you use. AI can even set the deadline, priority level, and more.
In need of a context? Let AI create the brief with all the specific details. Agentic workflows are today just as good as humans in problem-solving. It can plan, revise, and collaborate with others.
Here are the four agentic workflow patterns:
1. Reflection. Let’s say you’re writing a research paper. The first draft never looks good. Agentic workflows also have this logic. It’ll review its work results for errors, weaknesses, or structural issues. It iterates and continuously higher-quality results. No more single-pass approach.
2. Tool Use. Just like how humans need software, like Microsoft Word or Grammarly, to do and refine their work, AI does too. It delves into web search, databases, APIs, or execution environments. It not only relies on internal data - it keeps searching for the most up-to-date sources. That’s why the agentic workflows we use today can retrieve real-time information.
3. Planning. When writing a research paper, humans need to outline, draft sections, summarize findings, and perform iterative reviews. Humans break down complex tasks into smaller steps. AI also does that, executing and adapting sequential steps that enable problem-solving at scale.
4. Multi-Agent Collaboration. Like humans, AI works together. You may recall MASs. There’s an agent who does the drafting. Another will then do the due diligence, checking for accuracy and providing feedback for improvement. Since different specialists work on one project, the results you get are much richer and nuanced.
Real-life examples of agentic workflows
Agentic workflows are new technology, and it’s understandable that many people still cannot grasp how they work in real motion.
The truth is, agentic workflows have been delivering value across different business functions and industries. They do not only conform to one rigid structure like traditional automation.
Agentic workflows are highly dynamic. They base their judgment, action, and decision on real-time data. Even the slightest change is taken into account. There’s context to every step that the AI takes.
Here’s how AI takes it further in different fields:
Marketing (Social Media)
Agentic workflows can produce images, video, text, and everything in between. It’ll assign a couple of agents to plan, create, and optimize content in real-time.
An example case study is TikTok marketing, when a beauty brand launches a new lipstick line. Following the rule of thumb toward virality, the company wants to ride trending sounds and other cheat sheets.
AI will first do trend detection. It scans TikTok and even mines data for emerging trends, popular audios, viral challenges, and up-and-coming influencers relevant to the beauty niche.
Another AI agent will use those insights to create a content calendar. It’ll also decide what format works best for each post or reels - tutorials, before/after, and so on. It’ll even add details on the best timing to post for peak engagement.
AI can also create short- or long-form videos, down to the captions, voiceovers, avatars, and special effects. However, most companies feel like AI is not yet optimized here - and that’s because they haven’t aligned the AI with their brand tone.
Post-publication, the AI agent will not only stay silent. It’ll monitor metrics: traffic, engagement, conversion rates, RoI. If the content doesn’t become a hit, AI will reflect. It’ll perhaps switch the music, try a different hook style, or go into more creative lengths to entice the users.
But if the AI successfully hit the content jackpot, it’ll repurpose that golden workflow across content and channels. It’ll develop Instagram reels, YouTube shorts, or TikTok carousels. That way, you’ll know which strategy brings you the most money.
Finance (Blockchain Fraud)
Crypto has been taking the center stage these days - and threats of cyberattacks escalate just as quickly. Across billions of microtransactions, humans have an impossible time keeping up with fraud detection and compliance monitoring. But if left behind, financial risks like money laundering will seep in.
Agentic workflows will proactively respond when threats knock on the door. It’ll detect phishing scams, wash trading, flash-loan attacks, token mixing, or other suspicious activities. It can track millions of on-chain and off-chain transactions across blockchains.
Transactions that are seemingly red flags will be labeled. AI will refer to historical behavior, transaction volume, or other datapoints. If the data is found to be associated with malicious actors, AI will trigger a transaction or an account freeze.
An agent, part of the workflow, will then raise alerts to the security teams or developers. A compliance report will be filed, following the policy of EU MiCA, FATF Travel Rule, or anything that applies in your country.
To prevent future incidents, you can combine genetic AI with deep learning and reinforcement learning for adaptive, real-time detection.
Human Resources (Hiring)
Hiring makes up a significant portion of time in an organization. When a position stays vacant for a long time, your company risks losing millions or billions of dollars.
Agentic workflows can expedite the cycle for hiring, all the way from initial screening. An agent who acts as a researcher will be activated. Its role is to evaluate candidates’ skills and background to decide the best of the bunch that’ll go to the interview phase.
During the interview, a communicator agent will be triggered. Instead of the HR sending a long message to every candidate and scrambling the calendar to find date availability, AI will be the one to coordinate, set up the interview, and send reminders.
In the final selection, a quality control agent will be deployed. This AI will assess interview performance based on predefined criteria and select the one who truly matches the company’s needs.
As for contract creation, a creator agent will handle that. It will generate a professional job offer letter as well as onboarding documents.
Agentic workflow use cases across industries
I’ve walked you through the roles of agentic workflows across different organizational functions - but their use cases far extend that. Even if narrowed to one specific field, an agentic workflow can balance between concurrent responsibilities.
So, while I’ve shown how agentic workflows help transform departmental workflows, I’ll also give extra examples on how you can further use these programs in other spectrums of your work. At the same time, I’ll also draw examples from other industries I haven’t yet covered.
Here’s the full table:
Overall, no matter what industry you are in, agentic workflows have proven to:
- Fast-track deal closure by 30-40%
- Increase marketing-attributable pipeline by 25%
- Enhance productivity in cross-functional alignment by 45%
- Achieve measurable results within 30-90 days
Future trends of agentic workflows
AI might be a recent innovation, but it has made a groundbreaking shift, and more and more enterprises have begun embracing this technology. Many are even trying to get ahead of the competition with agentic workflow adoption.
Even in its current and standard form, agentic workflows have made waves across departments, organizations, and industries, as I’ve presented in the use cases and examples before.
As time goes by, these programs will become even more intelligent and powerful. Start implementing the technology now, even in small steps. Doing so gives you an edge in learning its strengths and limitations early. Those who delay risk a steep learning curve and competitive disadvantage, especially as agentic systems rapidly mature.
With that in mind, here are some future trends to get ahold of:
Refined Contextual Understanding
Day by day, with each given update, AI is becoming better at understanding context. Take ChatGPT, for instance. Back then, it could only process short prompts and give basic answers.
Now, ChatGPT can analyze long documents, maintain its memory over extended conversations, and even adapt tone and style based on your preferences.
In the future, AI will be able to recognize even subtle nuances in human languages. They’ll become expert conversational agents in customer service, employee onboarding, eCommerce, and beyond.
AI systems can increasingly adapt behavior based on inputs, context, or historical preferences—though true situational awareness remains a challenge.
When you need help making decisions for a specific marketing strategy, task prioritization in large projects, or adjusting investment portfolios, AI can offer insights or recommendations.
That said, human oversight is still critical. Treat AI like an assistant, not the ultimate decision maker.
Multimodal Model-Based Agents
The AI we have today is not only able to process texts, but also other forms of media: images, videos, and more. Likewise, agentic workflows enable visual AI tasks across logistics, medical imaging, military applications, and other fields imaginable.
The reason is because agentic workflows can detect objects, analyze scenes, and segment videos. Tools like APIs, machines, and crawlers further help these visual AI agents generate results faster and better.
To refine the output over time, agentic workflows use planning, feedback loops, and memory retrieval. The versatility of AI agents is visible in real-life scenarios. For instance, in social sciences, vision agents help support research, decision-making, and policy analysis.
Meanwhile, in engineering, AI helps with data analysis and research tasks in atmospheric sciences.
A year ago, analyzing visual datasets with AI felt impossible. Starting from today and years ahead, that’s about to change.
Optimized Revenue Process
Marketing and sales are often separated - when they’re two sides of the same coin. This disconnect leads to communication silos. Marketing doesn’t know how to effectively generate leads because it’s missing crucial insights from sales. And because the lead quality is poor, sales find it harder to close deals, missing revenue targets.
Agentic workflows can aggregate these two functions harmoniously. McKinsey reveals that organizations using analytics across the entire customer journey can improve marketing ROI by 15-20%.
Agentic workflows can also identify bottlenecks that the human eye doesn’t see. They recommend workflow adjustments in real-time, which can improve revenue by 15%, according to Gartner.
How to implement agentic AI workflows with Boltic
So, are you ready to take the leap? This section will show you how to get started with agentic workflows.
Here’s a template, optimized for every organization, that you can use as a reference in your starting point.
I’ll use Boltic.io, which is one of the best AI agentic workflow tools I’ve ever come across. It has received a 5/5 rating on G2. Many users appreciate its ease of use, comprehensive analytics, and AI integration across platforms: Slack, HubSpot, and more.
STEP 1: Define your objectives
Assess the business challenges that your human resources cannot solve. For instance, automating lead routing or detecting transaction anomalies. AI can perform these tasks with greater accuracy. Have a team meeting and establish measurable goals, such as:
- Hours of time saved
- % of error reduction
- % of improved forecast accuracy
STEP 2: Map existing workflows
Every company proudly uses its set of tools: CRMs, messaging tools, analytics, Google Docs, and Sheets.
Take a good look at each of these tools. How do they contribute to workflow, and with AI integration, how can you use them to their fullest potential? That’s where you’ll find areas where automation can deliver value.
STEP 3: Set up Boltic account and connections
Sign up for Boltic. Then, connect your relevant tools. I find it a blessing that Boltic connects to 100+ apps, including Notion, Slack, Google Drive, GitHub, etc. Not only that, Baltic can also provide real-time context and data flow without having to juggle between multiple platforms.
STEP 4: Choose and Configure AI Tools
This is where the fun begins - Boltic supports Grok, Claude, and OpenAI. Your job is to choose your favorite AI to integrate within your current workflow.
Once that’s out of the way, configure API credentials, temperature, model settings, and response constraints following your workflow requirements.
STEP 5: Design “Bolts” or Agentic Workflows
Boltic supports a low-code interface, so you can visually define multi-step workflows without any tech expertise. These include chaining triggers (such as a new invoice uploaded) to AI agents and downstream actions.
Moreover, you can incorporate memory retrieval, planning logic, and feedback loops. You’ll need to feed the AI so that it can learn and improve over time.
STEP 6: Enable Multi-Step Actions With Boltic MCP
Boltic has a special feature called MCP (Managed Context Provider). It can read live structured data and trigger actual actions, namely:
- Send reminders on Slack
- Update leads pipeline on HubSpot
- Record in the database
This tool is great for adding adaptability. It’ll also enable dynamic responses based on data changes and real-time events.
STEP 7: Pilot and Test
Begin with a single workflow. It could be an expense review or chat-based support automation. Along the way, validate agent behavior, ensure fallback mechanism, and refine decision logic. Do not forget to track the following KPIs:
- Time to resolution
- Accuracy
- Error rate
- User satisfaction
STEP 8: Scale Across Use Cases
Expand your pilot success to other workflows, such as inventory management, compliance review, or marketing lead scoring. Most importantly, coordinate across departments by integrating cross-functional data sources.
Follow this structured approach by Boltic, and see how you can implement agentic AI workflows autonomously and dynamically as they integrate with your existing systems.
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