What are AI agents? Use cases, benefits, and innovations for 2025

There’s no escaping the AI hype these days. It pervades the air we breathe.

AI is already in chatbots, recommendation engines, voice assistants, you name it. But when it comes to customer service, support agents are still manually grappling with irate customers firing off tickets. Teams are unable to keep up with the flood of incoming tickets.

The problem isn’t just that customers demand faster support—it’s that they expect smarter support. They want solutions now, and they want them personalized. That’s where AI agents come in.

Unfortunately, most companies are still playing catch-up, relying on legacy support models that simply can’t keep pace. It’s a recipe for churn.

This guide will walk you through what AI agents are, how they work, and why they’re becoming a critical part of the future of AI in customer service. Let’s dive in.

What is an AI agent?

AI agents are software programs that can understand environments, make decisions, and take action to reach specific goals without needing human help.

Unlike simple AI chatbots that follow scripted rules and decision trees, AI agents can understand context and process vast amounts of data to make decisions to accomplish predefined goals.

According to Gartner, by 2028, 33% of enterprise software applications will incorporate agentic AI capabilities, a significant increase from less than 1% in 2024. This shift is expected to enable 15% of day-to-day work decisions to be made autonomously—meaning AI systems that not only assist but also independently drive actions, outcomes, and continuous improvement across teams and workflows.

Key principles defining AI agents:

While most software performs tasks based strictly on given instructions, what makes intelligent agent AI unique is their ability to analyze, decide, and act—intelligently and autonomously.

Here are the core principles that set them apart:

• AI agents don’t operate in isolation. They tap into large language models (LLMs) and domain-specific datasets to understand natural language, product data, and past interactions. They also ingest data from multiple sources—like telemetry, customer conversations, internal docs, CRM, and Git—to build a 360-degree view of the issue before acting a complex tasks.
• Agent AI acts as an autonomous decision-maker, evaluating multiple possible actions and making rational decisions independently—without requiring constant human oversight.
• Unlike rule-based bots, Agent AI is goal-driven. It optimizes its actions toward predefined goals such as deflection, co-pilot, etc.
• Though Agent AI seeks data to generate contextually relevant answers, it still respects data partitioning rules, such as region-specific regulations or team-based visibility constraints (e.g., GDPR, SOC 2).

So, are AI agents here to replace human agents?

The answer lies somewhere between yes and no.

For instance, an AI agent can handle 1000s of inquiries any time of the day (or night) and ensure personalized responses for each. It’s a task human agents simply can’t scale to handle efficiently.

But, AI agents aren’t equipped to replace the human elements that are intrinsic to customer support. When it comes to situations that require emotional intelligence and nuanced judgment, like dealing with difficult customers, human agents are indispensable.

Instead of thinking of AI agents as replacements, consider them force multipliers. Together, they form a tour de force: AI for efficiency, humans for depth.

So, it’s not AI vs. human—it’s AI + human.

Recommended reading: How DevRev’s Agentic AI is powering up human & AI.

How do intelligent AI agents work?

AI agents work by combining artificial intelligence technologies such as natural language processing (NLP), machine learning (ML), large language models (LLMs), and data analysis into a seamless process to operate autonomously, learn continuously, and adapt to ever-changing environments.

Here’s a detailed breakdown of how they operate:

1) Goal initialization: Setting the objective

Every AI agent starts with goals set by human programmers and leaders to guide the AI agent’s entire decision-making process. These goals could be as simple as answering customer questions or as complex as optimizing a supply chain.

Example: The AI agent might be programmed to decrease response times in customer support by 50% or flag potential billing issues before they occur.

2) Perception: Gathering information from the environment

AI agents gather real-time data, whether it’s customer inquiries, website activity, or system performance metrics. Every click, every question, every issue reported becomes valuable input.

Example: If a customer asks, “Why is our data not syncing?” the AI agent instantly perceives this question and understands it as a potential system issue requiring deeper exploration.

3) Data processing: Making sense of inputs

Once the AI agent has perceived its environment, it processes that information using NLP to understand the intent behind the input. This might involve the agent processing customer complaints, identifying frequent issues, and even understanding the tone of the conversation.

Example: Going back to the previous example, if the customer asks a query like “data not syncing,” the AI agent grasps the context of a failed system integration and what actions might resolve it.

4) Decision-making: Selecting the best action

Once the AI agent has all the necessary data, it engages its decision-making capabilities. Based on predefined rules, learned experiences, and probabilistic models, it decides the best course of action. If a solution worked before, they are likely to prioritize it again, but if new data suggests a better approach, they can pivot.

Example: If the AI agent recognizes that the syncing issue stems from exceeding an API limit, it could decide to guide the user through resolving the problem or escalate it to a human if the situation requires deeper troubleshooting.

5) Executing selected action: Taking the necessary steps

Once a decision is made, the AI agent acts. Action could mean providing a solution to a customer, automating a task, or adjusting a process within a system.

Example: In customer service, this might be generating a response like, “Your data sync failed due to exceeding your API call limit. Would you like to increase your API quota or adjust your sync frequency?” In a more technical environment, an AI agent managing a cloud infrastructure might automatically adjust server resources to handle increased traffic based on real-time monitoring.

6) Feedback loop: Learning from outcomes

Continuous learning is what makes AI agents shine. Every time they take an action, they assess the outcome. Did the issue get resolved successfully? Was the customer satisfied? Did a particular solution outperform others? This feedback is critical because it allows the AI agent to improve its decision-making over time.

Example: If a customer repeatedly encounters the same issue, the AI agent not only remembers the customer’s previous interactions but adjusts its approach to provide faster, more efficient resolutions in the future. This learning happens in real-time, allowing AI agents to become more intelligent with every use.

7) Adaptation and refinement: Becoming more efficient over time

AI agents are adaptive—they evolve over time. As they gather more data and process more feedback, they refine their strategies. This is particularly powerful in complex, dynamic environments like customer support, where customer needs and behaviors change over time.

Example: An AI agent deployed for customer success might notice that customers in a certain industry are constantly asking for a specific feature. The agent can adjust its responses, preemptively guiding future customers through that feature to reduce future tickets.

Recommended reading: See how DevRev envisions the future of AI Agent.

Types of agents AI model

AI agents are categorized into 5 key types, namely simple reflex agents, model-based agents, goal-based agents, utility-based agents, and learning agents. These AI agents system differ in how they perceive their environment, adapt to changes, and take action.

Each type is designed to tackle different problems, ranging from simple, predefined tasks to complex decision-making processes. Let’s see each of these types of AI agents in detail:

1) Simple reflex agents:

Simple reflex agents are the most basic form of AI model. They operate on a straightforward “condition-action” rule set, responding directly to the current situation based on what they perceive. They don’t have memory or the ability to learn, meaning they can’t make decisions based on past experiences, complex tasks or anticipate future states.

2) Model-based reflex agents:

Unlike simple reflex agents, these AI agents can make decisions based on what’s currently happening and what has happened before. By maintaining an internal state of the environment, they can infer information that may not be immediately visible, which is crucial in more complex scenarios where the full picture isn’t readily available.

3) Goal-based agents:

These AI agents aren’t simply reacting—they’re actively pursuing goals. They search for action sequences that reach their goal and plan these actions before acting on them. For instance, say a goal-based AI agent is given the goal of managing a customer’s onboarding process. If a new customer hasn’t completed the steps needed to fully integrate with the platform, the AI agent can proactively reach out, offering personalized tutorials or support.

4) Utility-based agents:

Utility-based agents not only pursue goals but also evaluate how desirable each outcome is. This is done through a utility function, which helps the agent measure how successful or beneficial each decision will be. When faced with multiple possible actions, a utility-based agent chooses the one that maximizes the outcome based on a calculated preference scale.

5) Learning agents:

These AI agents don’t rely solely on pre-programmed knowledge but adapt by learning from their experiences. They incorporate feedback mechanisms that allow them to analyze data to identify patterns and refine their understanding of the environment to improve their performance.

Differentiating AI agents from AI assistants and bots

While AI agents, AI assistants, and bots may seem similar on the surface, they differ in depth, autonomy, and intelligence. Understanding these differences is key to choosing the right solution for your business or workflow.

Here’s the clear comparison breakdown based on distinct characteristics:

5 key benefits of autonomous AI agents

Whether you’re a customer support professional, software developer, sales rep, or product manager, AI agents can help you execute your repetitive tasks better.

Here are some familiar pain points from different teams to start off with:

• Support managers scramble to diagnose the root causes of service-level agreement (SLA) breaches due to siloed tools and incomplete ticket histories.
• New support agents feel overwhelmed by outdated training materials and scattered knowledge, making ramp-up slow and painful.
• Developers lose precious time sifting through mountains of documentation and lose velocity as they dig through outdated or fragmented documentation to understand what’s been built.
• Sales reps often feel unprepared for demo calls due to lack of insight into latest product documentation
• Product managers struggle to prioritize issues when planning sprints with incomplete feedback lacking clarity.

These challenges slow down and frustrate your team. But with intelligent agents, the game changes entirely. Here’s how:

1) AI agents improve operational efficiency

AI agents can analyze enormous amounts of data in real time. They can surface patterns, flagging recurring issues, performance bottlenecks, or even individual customer complaints that might be driving the SLA breach.

Let’s say an API outage is causing delays across the board. Instead of manually piecing together this puzzle, the intelligent agent AI scans through the ticket backlog and identifies the specific technical failures that caused the delay, delivering a detailed report to your dashboard.

Not only does the AI agent technology find the problem faster, but it also proactively suggests solutions, allowing the support manager to take immediate corrective action for the SLA breach and even provide predictive support to customers.

DevRev has enabled us to streamline access to technical documentation, automate common developer queries, and bridge the gap between support and engineering. This has been essential as we scaled from 10M to 300M daily participant sessions without growing our support headcount.

Gilad ShrikiCo-Founder @ Descope

2) AI agents enhance onboarding processes

Onboarding a new support agent requires lengthy documentation reviews, shadowing senior agents, and spending days or even weeks learning the nuances of the company’s processes. All this time spent learning could be better spent solving customer problems, right?

Here’s where AI agents step in with instant, real-time guidance. Instead of sifting through training manuals, new agents can ask the AI: “How do I escalate a ticket in this situation?” With its genAI search capabilities, AI agent not only provides the correct protocol, but it also explains why that protocol is in place.

3) AI agents accelerate development workflows

For a developer starting work on a new product enhancement, it often feels like being dropped into an endless sea of data — bug reports, customer feedback, previous code iterations — it’s a lot to take in. Finding the information needed to kickstart the project is time-consuming and, frankly, frustrating.

But AI agents can help developers cut through the clutter. Instead of searching through thousands of support tickets and customer reviews, an AI agent gathers the most relevant data. It identifies the top customer complaints regarding the feature, shows related bug fixes, and even highlights the most common usage patterns. This empowers developers to start coding faster and deliver improvements on time.

4) AI agents streamline sales preparation

For a sales rep, the stakes are always high in a second or third call with a prospect. The initial interest has already been captured—now it’s time to drill down into the prospect’s specific pain points and demonstrate exactly how the product can solve their problems.

Preparing for these calls by poring over CRM data, past emails, or demo call notes—especially with a packed schedule—can be overwhelming.

But AI agents can automatically compile all relevant insights—previous interactions, product usage metrics, and even the prospect’s most viewed website pages. In an instant, sales reps have a comprehensive overview of what matters most to your prospect.

The result? The sales pitch becomes hyper-relevant to the customer’s needs, increasing the chances of closing the deal.

5) AI agents improve sprint prioritization

For a product manager, sprint planning is often the most challenging part of the job—constantly balancing feature requests, bug fixes, and customer feedback, all while trying to make sure the team isn’t overloaded.

That’s where AI agents come in. Integrating AI agent can analyze incoming tickets, customer requests, and team performance metrics, providing a data-backed roadmap for the sprint.

Let’s say a specific bug has been affecting multiple high-value customers. AI agents will automatically prioritize this issue, ensuring it gets fixed in the next sprint. On the other hand, if a new feature request is gaining momentum based on customer feedback, the AI will highlight it, helping product managers align sprint goals with customer demand.

Best practices for implementing AI Agents in your business

1. Start with focused use cases

Before implementing an AI agent, start by identifying specific, repetitive tasks that consume significant agent time and effort. For example, use AI agents to auto-summarize long customer conversations across multiple platforms or escalate and route incoming tickets based on customer tone, urgency, and topic.

This focused approach makes it easier to measure outcomes, demonstrate early ROI, and reduce the risks associated with broader deployment.

2. Mitigate AI from hallucinating

AI agents depend heavily on the data they’re trained on. However, at times, it can generate inaccurate or misleading information. For instance, in a notable incident, Google’s AI Overview suggested that to prevent cheese from sliding off homemade pizza, one could mix about 1/8 cup of non-toxic glue into the sauce. This advice originated from a satirical comment on Reddit but was presented by the AI as a serious recommendation.

This highlights how AI systems can confidently provide incorrect answers—a phenomenon known as “hallucinations.” To mitigate these, implement Retrieval-Augmented Generation (RAG), which enhances AI agents by retrieving relevant, accurate data from external databases or knowledge repositories.

3. Evaluate for reliability

As AI agents work autonomously on complex workflows with the data they hold, it is not a one-time set-and-forget process. User intent evolves, product data changes, and support content gets updated regularly; it needs to be monitored by humans for reliability, relevance, and accuracy.

So, it is good to use AI systems that come with built-in observability—ones that track what the agent said, why it said it, and whether it was helpful. This level of transparency lets teams trace errors, close knowledge gaps quickly, and continuously fine-tune performance.

4. Process under human shadow to have control on complex workflows

Though AI can handle large volumes of data and low-stakes tasks, what about the complex issues? We’ve already seen the AI hallucination. At times, AI agents can misread intent, miss nuances, or occasionally return biased or incorrect outputs.

We must have encountered these issues multiple times with ChatGPT. To avoid this from happening again, it is important to strike a balance between human oversight and agent AI processes.

There are two simple ways to do this:

• Human-in-the-loop (HITL): Humans review and approve key decisions, such as handling critical escalations or publishing product changes.
• Human-on-the-loop (HOTL): AI runs on its own, but humans monitor outcomes and can jump in when needed.

5. Choose a pricing model

With intelligent AI agents around, you must think that implementing AI in your business to automate routine tasks is expensive—considering the cost of implementation, usage, and maintenance. Gone are the days of such expensive implementation, says Manoj Aggarwal, co-founder & president of DevRev, in the Effortless conference 2025:

That’s the mindset businesses need to adopt. Instead of being locked into complex contracts or overpaying for limited functionality, choose a pricing model that’s flexible and grows with you—so you’re not paying for what you don’t use.

Challenges and solutions in implementing AI agent system

While Agent AI promises speed, efficiency, and scalability, its implementation comes with its own set of challenges. However, you can bypass it with careful planning, incremental deployment, and ongoing optimization.

Let’s scan the challenges of AI agents to navigate them proactively:

1. Integration complexity with the existing legacy system

AI agents need to work with existing tools—like CRMs, ERPs, or databases. But many of these legacy systems aren’t built for AI compatibility. They might lack APIs or modern interfaces and require custom APIs, middleware, or process changes, which can be time-consuming, costly, and disruptive to daily operations.

Solution:

Opt for platforms that offer native support for modern systems and can act as a central layer connecting tools and teams. This reduces the need for heavy custom development and keeps workflows smooth during rollout.

2. Data privacy challenges

AI agents depend on high-quality data to function properly. If your data is outdated, incomplete, or siloed, the AI won’t perform well. Additionally, inconsistent or biased data can lead to poor decision-making or unreliable automation.

Solution:
Use a unified system that brings teams, product, support, and customer data together in one place. Centralizing data improves AI output and gives teams a single source of truth.

3. Scalability and performance

As AI agents are given more responsibilities, taking on a large volume of requests or integrating with multiple custom legacy systems can strain and crash the infrastructure, making it challenging to maintain speed, accuracy, and reliability in larger environments.

Solution:
Choose platforms built on modern, cloud-native architecture so they can scale automatically with usage. Bonus points if the system is modular, allowing you to roll out AI features gradually based on needs.

4. Privacy and security

With a lot of sensitive data in the business, an AI agent should be strong and secure enough to handle that data without any data breach or legal compliance issues. Without proper safeguards, there’s a risk of data leaks, unauthorized access, or non-compliance with regulations like GDPR.

Solution:
Use platforms with SOC 2 Type II certification, role-based access, audit trails, and data encryption. This ensures enterprise-grade security and compliance with regulations like GDPR.

5. Ethical consideration and bias

AI can unintentionally reflect human biases present in its training data. This can lead to unfair decisions—such as biased hiring or unequal customer service experiences.

Solution:
Use real-time user behavior tracking (like session replays and heatmaps) to detect and fix biased patterns quickly. Continuous feedback helps keep AI fair and ethical.

6. Maintenance and updates

No AI model is a set-and-forget type. As businesses evolve, so do data patterns. AI models need continuous monitoring, retraining, and updating to stay effective as business needs and user behavior evolve.

Solution:
Look for AI agents offering self-learning, or allow easy retraining based on feedback and usage trends. Even better if they include pre-built workflows that can evolve over time.

7. Cost and investment

While AI agents can save time and money in the long run, the initial setup cost can be high. Expenses include licensing, data preparation, integration, infrastructure upgrades, and hiring or training skilled professionals to manage the system.

Solution:
Start with a platform that offers built-in AI agents, so you don’t need to assemble multiple tools. Look for transparent pricing and AI that delivers value from day one—like automating ticket triage or reducing handoffs.

3 powerful use cases for AI agents in business

Agent AI is transforming departments across the board—from support to operations. Here are three powerful use cases of AI agents that are already driving impact.

1. Customer service

In most businesses, customer service is the one prominent area where AI is widely implemented. With customers expecting to receive fast, contextual, consistent support 24/7, customer support teams often face burnout from repetitive queries, high ticket volumes, and disparate data.

How Agentic AI resolves it:

• AI agents can generate real-time summaries of multi-channel customer interactions, helping agents get up to speed quickly without reading through long threads.
• Intelligent AI auto-triage and assign tickets to a human based on past patterns, customer sentiments, and business priority.
• When handling an unfamiliar issue, the autonomous agent AI can retrieve similar solved cases from the knowledge base or past tickets to help the agent respond faster.
• AI auto-drafts a full response to the customer queries with full context pulling from CRM, product telemetry, and prior ticket history—so agents never start from scratch.
• Spot emerging issues early through real-time signals from tickets, feedback, and product data—triggering proactive resolution before it escalates.
• Resolves high-volume, low-complexity tickets (L1 and L2) queries through pre-trained flows and knowledge integration.

Example of AI agents in action:

As customer expectations grew, for instance, 24/7 resolutions, Tough Trucks—a logistics tech firm—struggled with rising repetitive tickets and stretched support cycles. Looking for a scalable solution to reduce manual workload and improve response times, they implemented DevRev’s AI. With real-time intent detection, contextual deflection, and smart triage, the AI handled recurring queries autonomously—freeing up human agents for complex issues. This led to a 60% drop in ticket volume, faster first responses, and zero agent burnout during peak periods.

Recommended reading: How Agent AI solves top customer support challenges

2. IT support

Modern IT teams are under pressure in handling high volumes of repetitive internal support tickets—such as password resets, access requests, how the process works, integration, or troubleshooting VPN issues, which becomes especially challenging as organizations scale.

How Agentic AI resolves it:

• Agentic AI auto-classifies IT issues based on historical data and routes tickets to the right team.
• AI agents continuously learn from resolved tickets and interactions to update and expand the knowledge base with accurate, context-rich solutions.
• AI agents analyze past tickets to identify recurring issues and use time-series forecasting to predict and prevent future incidents.
• AI triggers automated scripts via Robotic Process Automation (RPA) to solve common issues (e.g., restarting servers, clearing cache) without human intervention.

Examples of Agent AI:

Deutsche Telekom, a telecommunication giant with 80,000 employees in Germany, faced a challenge in providing a quick and efficient response to the internal queries related to HR policies, benefits, and employee work policies. Traditional methods were time-consuming and often lead to delays in response.

To address this, the telecom company rolled out an Agent AI – ask me anything agent to the employees, called askT. The result? About 10,000 employees are using AI agents in a week to get instant answers to questions about internal policies, benefits, and services.

3. Product development

Product and engineering teams often work in silos from support, leading to delays in resolving critical customer issues and building the product that truly matters.

AI agents system make this process seamless by transforming how product workflows are managed—from idea intake to release.

How Agentic AI resolves it:

• AI agents summarize Slack or email threads and auto-update tasks, tickets, and dependencies—no manual entry needed.
• Agents cluster feedback, analyze customer impact, and suggest what to build next based on real-time signals.
• AI flags delays, misaligned resources, and blockers by continuously analyzing sprints, Git, and workload patterns.
• Once the product is developed, agents draft release notes and notify relevant stakeholders—keeping support and users informed without added effort.

Example of Agent AI:

When Shipsy, a global logistics SaaS company, scaled its operations, it found that the teams were operating in siloes, which delayed issue resolution and made it harder to connect customer feedback to product development. Looking for an answer, they turned to DevRev to build custom automation to streamline support workflow by connecting customer feedback directly to their roadmap.

With DevRev’s AI agent, they not only unified the teams but also built custom automations such as aging ticket alerts, SLA breach triggers, and approval funnels for Product Managers—to stay ahead of delays and focus on building. These enhancements not only reduced resolution times but also improved collaboration across support and product teams.

Read more: How Shipsy scaled support and reclaimed time to build.

DevRev not just helped implement their Support app but also guided us in improving our support processes. That's something we truly appreciate

Dhruv AgrawalCOO & Co-founder, Shipsy

How AI agents enhance industry-specific solutions

How AI agents are leveling up new tech

With challenges to overcome, such as upfront investment, reliability, and the need for a skilled person. AI tools has rapidly progressed from traditional analytical AI to generative AI and now to AI agents.

Here are some promising developments:

1. Agentic memory and contextual awareness

Users are tired of repeating queries from the start to every human agent they contact. Agentic AI has evolved its contextual understanding by incorporating RAG into its system. This allows agents to retain and recall contextual information over extended interactions. This capability ensures more coherent and personalized user experiences, as agents can reference past interactions regardless of the channel for the current responses.

2. Multimodal interaction interfaces

Unlike traditional AI, intelligent AI agents have evolved to process and handle various types of data, such as text, images, videos, and audio, allowing users to interact more meaningfully.

These advancements underscore the importance of multimodal capabilities in AI agents, which allow them to understand and respond more effectively to complex, real-world scenarios.

When OpenAI launched ChatGPT in November 2022, it could only read and write text using Natural Language Processing (NLP). Since then, AI has improved by adding the ability to work with different types of data. DALL-E was OpenAI’s first multimodal model, which later developed into ChatGPT-4 and GPT-4.5. For instance, an agent can interpret a user’s spoken query, analyze an accompanying image, and provide a comprehensive response that includes visual aids.

3. Hybrid intelligence system

With increasingly complex businesses, which can’t be solved by deep learning alone and need something more, a hybrid intelligence system pops in. It is a system that combines multiple AI techniques like symbolic reasoning, neural networks, machine learning, and knowledge graphs to deliver smarter, more dependable outcomes.

Let’s break it down:

• Symbolic AI + Machine Learning is akin to giving your AI both a brain and instincts. Symbolic AI relies on established rules and logic, while machine learning focuses on identifying patterns in data. Each approach has its limitations, but when used together, they produce agents capable of reasoning through rules and learning from data.
• Neural Networks + knowledge Graphs help AI respond at a deeper level. Neural networks are great at recognizing patterns in images, text, and audio, but they don’t “get” the relationships between things. However, it’s not the same with Knowledge Graphs, where it understands the structure and meaning, which helps AI models understand what’s going on and respond more specific and deeper.
• Deep Learning + Reinforcement Learning makes the agents start helping teams resolve issues faster and smarter. Deep learning spots patterns. Reinforcement learning figures out what actions work best over time. When paired, you get AI agents that don’t just react—they learn what works and keep getting better.

4. Multi-agent collaboration

At times, one agent isn’t enough to perform the required tasks. A multi-agent system (MAS) composed of multiple AI agents is required to perform one task on behalf of a user or another system.

These agents communicate, share goals, and coordinate tasks, much like a team. One agent might collect data, another might analyze it, a third might act on it—all happening in parallel to get work done faster and more accurately.

For example, in a product scenario: one agent can detect a bug, another notifies the product team, and a third drafts the status to update the customers. All other agents AI work together in a line. This setup makes systems more scalable, responsive, and autonomous, especially in complex business environments.

The future of customer service is Agentic AI

Andrew Ng, renowned computer scientist and a pioneer in machine learning, had this to say about AI agents in a lecture: “The set of tasks that AI can do will expand dramatically because of agentic workflows.”

Given the promise it holds, organizations worldwide are eager to adopt AI agents. A Capgemini Research Institute study reveals that 82% of surveyed organizations intend to integrate them within 1–3 years.

So, it’s evident that our future work experience will be one where AI agents perform tasks autonomously on our behalf. AI agents will be the way by which modern organizations will support customers, build products, and drive revenue.

To enable this profound change, AI agents will require a platform that provides context by unifying data and information around products and customers and make workflow integrations seamless. And that platform is DevRev.

At the heart of DevRev is its robust knowledge graph, in which data from legacy systems is continuously being fed in real time to an interlinked network of customer, product, employee, work, user, and session records. This organization of structured and unstructured data connects the work you do to its relationship with your product and customers.

DevRev’s knowledge graph is the data layer powering its AgentOS platform, which connects humans and AI agents on one unified system.

Agents are armed with the data and tools they need to perform a wide range of tasks efficiently, and humans are provided with a modern application interface to both define the skills of their agents effortlessly and collaborate with them in real time.

The result? You can reduce customer ticket resolution times by up to 50% and boost agent productivity.

Ready to enter the future with DevRev’s AI Agents? Book a demo now!