What if you could walk into the office every morning to find the time-consuming work already done? Emails are drafted, schedules are optimized, and inventory orders are placed—all before anyone has clocked in. That's the promise of agentic AI: more efficiency, less manual effort.
Agentic AI systems take on labor-intensive tasks by making decisions and taking action without someone having to micromanage at every step. It’s often implemented through intelligent automation and AI agents that handle complex end-to-end workflows with as much or as little oversight as you choose. They automate repetitive processes and adapt to changing circumstances, actively working toward defined goals.
Agentic AI is set to have a significant impact on how businesses work. Gartner predicts that a third of enterprise software apps will include agentic AI by 2028, autonomously making 15% of routine decisions that currently use up human attention. While these systems operate independently, they work within carefully defined parameters, freeing teams to focus where their skills matter most.
So what exactly is agentic AI, and how can you leverage it for your business? This guide breaks down how you can use it to streamline workflows and support your team in focusing on strategic priorities.
What is agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously or semi-autonomously make decisions and complete tasks without constant human oversight. These systems are dynamic—they adapt to changes as they happen and respond to new information in real time.
When an email system learns which messages are urgent and automatically flags them for immediate attention, you're experiencing agentic AI in action. Instead of rigidly following pre-set rules, these AI assistants adjust their actions based on real-world patterns to improve their performance.
You can design your AI agent to act semi-autonomously, giving you the ability to audit results and control important decisions while AI handles the time-consuming tasks. Think of it as your AI copilot rather than a system on full autopilot—AI agents manage repetitive work, make decisions based on real-time data, and prepare the groundwork for you to make the final call. You can set rules or thresholds for when AI takes action versus when it escalates decisions to humans, with whatever level of audit control you need.
Agentic AI systems share four characteristics:
Autonomy: They can make decisions without human input. For example, an AI assistant may schedule meetings by finding the best time slots for everyone, considering their calendars and preferences. They can also be designed to support humans who will make final decisions, such as presenting the most qualified job candidates for HR managers to review, without auto-rejecting anyone.
Adaptability: They respond dynamically to changing conditions and unexpected events. For instance, a logistics AI agent may handle minor route adjustments automatically while flagging major changes—like rerouting an entire fleet due to severe weather—for manager review.
Goal-focused: They work toward specific outcomes based on objectives that you set. For example, an AI social media manager might analyze audience activity patterns to schedule posts during high-traffic periods, working toward its goal of increasing post engagement.
Learning capabilities: They analyze results and feedback to improve their decision-making capability over time. For example, an AI writing assistant learns from your editing patterns to better match your writing style in future document drafts.
Many businesses already use artificial intelligence that operates autonomously without drawing attention to it. Dynamic pricing strategies are a prime example—when you book an Airbnb or call an Uber, you may find prices to be higher than usual for that time of year or day. Retail, e-commerce, and platform-based businesses like Airbnb and Uber use agentic AI to monitor demand and inventory levels to adjust prices, balancing customer demand with service availability.
Benefits of agentic AI for your business
At first glance, the benefits of agentic AI may sound similar to automation—it reduces workload, increases efficiency, and frees up your team for higher-value work. Unlike fixed automation that simply follows rules, agentic AI actively works toward your goals while responding to real-world conditions.
Frees up time for high-priority tasks: Agentic AI handles repetitive tasks like updating client records, processing invoices, and managing routine approvals, relieving employees of labor-intensive work. This gives your team more time to focus on more strategic or high-priority tasks.
Adapts to changing situations in real time: The AI system monitors conditions and data, adjusting its actions or recommendations accordingly. This reduces the time your team needs to spend monitoring and adjusting processes manually.
Focuses on the goal: Rather than just completing isolated tasks, the AI system makes decisions or prepares recommendations that support your broader objectives. Each action or recommendation directly contributes to achieving your business goals.
Works with current information: The AI system processes data as it comes in, making decisions or suggesting actions based on what's happening right now. This eliminates the need for manual data gathering and analysis while minimizing errors and improving decision quality.
Scales with your business: The AI system can manage increasing amounts of data and automate more tasks to grow alongside your business. Your team won't face growing administrative burdens as operations expand, and you won’t need to constantly rebuild or reconfigure your processes.
Works as part of a team: Multiple AI agents can work together, each handling different tasks while coordinating their efforts. This creates more efficient, interconnected workflows across your organization and reduces manual handoffs.
How does agentic AI work?
Although agentic AI operates autonomously or semi-autonomously, it relies on human-defined goals to guide its actions. You decide what you want to achieve: say, “keep $50,000 worth of inventory in stock” or “reduce customer support wait time by 40%.” These initial instructions serve as directions for the AI agent so that its actions serve your business purposes.
An agentic workflow follows a multi-step process:
Processes data: The system gathers and integrates multiple data types: structured data (databases, spreadsheets, CRM), unstructured data (emails, reviews), external platform data (supplier portals, logistics dashboards), real-time information (live traffic updates, weather conditions), historical data (purchase history, sales trends), predictive insights, and sensor data (motion detectors, location data, temperature readings).
Analyzes data: The system evaluates both historical and live data in real time to assess current conditions. Based on this analysis, it identifies what the most effective course of action will be to achieve your goals.
Executes tasks: The system carries out specific tasks across different platforms—sending communications, updating records, placing orders, adjusting schedules, or modifying settings. Based on how it's configured, it can either execute these actions directly or prepare them for human approval, adapting to any changes that might occur.
Learns over time: The system continuously reviews the results of its actions to improve its decision-making process. Over time, it recognizes outcomes, feedback, and new patterns to refine its approach and perform better in the future.
Technologies that power agentic AI
An agentic AI workflow relies on a combination of technologies to function effectively.
Machine Learning (ML) allows agentic AI to process large datasets, identify patterns and trends, and make decisions based on the data analyzed. This enables the system's ability to learn from experience and adapt its approach over time.
Natural Language Processing (NLP) is how agentic AI understands, interprets, and generates human language. It’s how the system analyzes text or speech, understands context, and responds appropriately to instructions and user intent.
Large Language Models (LLMs) are a type of NLP technology—sophisticated machine learning models trained on vast datasets of text using deep learning techniques. They handle complex language-related tasks like understanding context and analyzing sentiment, making them particularly effective for processing and generating text within AI systems.
How agentic AI differs from automation and other AI types
Before implementing agentic AI, it's important to understand how it differs from other types of AI and automation tools you might already use. This will help you identify where agentic AI can add the most value to your existing systems. While these technologies might seem similar on the surface, they function quite differently from truly agentic systems. Here's how to tell them apart.
Agentic AI vs traditional automation
While traditional automation handles predictable tasks by following fixed rules, agentic AI can analyze multiple factors and adapt to changing situations to achieve specific goals.
Traditional automation operates like a switch—you turn it on, and something happens. In this case, when a trigger occurs, it initiates the automation workflow to perform an action. If inventory drops below 10 units, order 50 more. These systems follow pre-defined rules and instructions to complete specific tasks. They rely on if/then logic, meaning they act only when a specific trigger occurs, performing a specific task without analyzing the bigger picture or adapting to any real-world changes.
Agentic AI, by contrast, evaluates beyond single triggers and actions. Take that same inventory system: an AI agent analyzes sales patterns, seasonal trends, and current demand. When stock runs low, it might suggest ordering 100 units instead of the standard 50 based on climbing sales trends, leaving you to confirm the decision or let the AI execute it autonomously. The system goes beyond following if/then instructions, instead adapting its actions based on changing conditions—an unusual spike in sales—to meet its core goal of maintaining optimal inventory levels.
Agentic AI vs generative AI
While generative AI creates content on demand, agentic AI actively manages processes by evaluating situations, making decisions, and taking action based on real-time data. These systems can work together, with agentic AI deciding when and how to use generative AI's content creation capabilities.
Generative AI models like ChatGPT and DALL-E produce text, images, and code in response to specific AI prompts. Unless connected to tools with web browsing capabilities, these models rely on older training data and can't independently update their knowledge or respond to new information in real time.
Agentic AI serves a broader purpose—it evaluates the situation, determines what needs to be done and, if programmed to do so, takes action. These systems can use generative AI tools as part of their workflow, but the key difference lies in managing end-to-end processes and making decisions in dynamic environments.
For example, in a customer service operation, a generative AI tool may draft email responses when prompted. An agentic AI system can actively manage the entire process—it monitors incoming tickets, prioritizes urgent issues, and directs the generative AI to craft appropriate responses. It can send responses for routine queries automatically while flagging complex issues for human review.
Agentic AI vs predictive AI
While predictive AI forecasts what might happen based on previous patterns, agentic AI uses these predictions alongside real-time information to make decisions and take action.
Predictive AI analyzes historical data to identify patterns and trends, making educated guesses about future events. It doesn’t act on these predictions—it’s only providing insights based on what has already happened, leaving the responsibility of decision-making and action to humans or other systems. Predictive AI's accuracy depends on the quality and completeness of the historical data it analyzes.
Agentic AI builds on these predictive capabilities by combining historical insights with real-time data to execute tasks. It both analyzes patterns and adapts to any changing conditions in real time.
Take supply chain management. A predictive AI system might identify potential delivery delays based on historical transportation data and weather forecasts, then notify a manager or a connected AI agent. An agentic AI system might not only use these predictions but also incorporate real-time traffic conditions, driver availability, and fuel prices to provide more accurate, current insights. If programmed to do so, it may also automatically reassign routes, optimize delivery schedules, and send updates to customers.
How to deploy agentic AI in your business
Getting started with agentic AI begins with understanding your business needs and having the right tools in place. Before implementation, you’ll choose the level of autonomy—from AI that handles entire processes independently to systems that analyze data and present options for your approval. Here's how to approach it step by step.
Identify pain points
Look for routine, time-consuming tasks that follow consistent patterns. These are ideal for agentic AI to handle entirely—whether it's copying data between systems, scheduling appointments, or sending follow-up emails. You can also identify processes that require consistent decision-making but don't need human creativity or judgment, where the system can support your team with data-driven recommendations.
Define clear goals
Once you’ve identified the pain points, set specific objectives for what you want to achieve with agentic AI systems. Set measurable goals like “review incoming support tickets, resolve common issues automatically, and flag complex cases for human review within 10 minutes.” These concrete goals will help you determine if the agentic AI system is delivering the intended value so you can adjust targets or workflows as needed.
Assess your business’s data readiness
Review where your business information lives. It might be siloed across email, spreadsheets, and various apps, making it difficult for AI systems to use effectively. For agentic AI to perform well, your data needs to be accessible, accurate, and well-organized. Start by consolidating your data sources and addressing issues like duplicates, inconsistencies, or outdated records. Maintaining good data hygiene allows your AI systems to work with up-to-date and reliable information, supporting better decisions and actions.
Select tools to implement agentic AI
Agentic AI is implemented through two key tools: intelligent automation and AI agents. Intelligent automation combines AI with automation to support teams by handling repetitive tasks, allowing humans to focus on decisions that require judgment and expertise. A no code platform like Glide can make this technology accessible through intelligent automation tools and specialized AI agents.
Intelligent automation tools can integrate with your existing software and data, making them practical solutions for real business needs. You can create custom workflows that match your specific processes, whether it’s automating routine data tasks or building AI-powered applications. This lets you implement solutions quickly without extensive development resources, whether you're a small business building core tools or a larger enterprise creating specialized department solutions.
Marketing agency Mintleads tripled the amount of deals they closed every month using intelligent automation. Their sales team uses a simple but powerful workflow: after each call, the system automatically collects prospect information, writes up call notes, and creates personalized pitch emails ready to send. Sales reps just review and click send, getting detailed proposals to prospects while conversations are fresh. By cutting out hours of manual work, the team fits more calls into each day. The results speak for themselves: 1,500% growth in just 18 months.
What are AI agents?
Learn more about how to deploy agentic AIAI agents can be set up for specific tasks in your processes, receiving input (like a contract or invoice), completing its assigned work, and producing output for human review. For example, an invoice processing AI agent can extract invoice information and send the details to your accounting software. Your team just has to review the results, cutting invoice processing times by 30%.
You can also link AI agents in a workflow for greater efficiency. When you connect the invoice processing AI agent with a contract management AI agent, new vendors from signed contracts can automatically be added to both agents at the same time. This streamlines operations by keeping tasks and data aligned across systems.
Get started with agentic AI
Many businesses rely on AI assistants and automation to analyze data, manage campaigns, and handle customer support. But agentic AI marks a significant shift—it can handle entire processes on its own or work alongside your team to automate routine tasks that keep them from doing more.
AI agents from Glide make this technology accessible to businesses of all sizes. Each agent is a specialized tool that transforms a specific business process, turning time-consuming manual work into streamlined, goal-driven operations. A resume screening AI agent for HR teams identifies the most qualified candidates from hundreds of applications, while an inspections agent speeds up inspections by analyzing quality and generating a report for your team to approve.
Agents can be tailored specifically to work within your processes, integrate with your existing tools, and support the goals of your organization. With Glide, you can provision custom agents that connect with your existing systems—giving you powerful, focused solutions within weeks.
