AI agents are becoming essential to how work gets done. These intelligent, autonomous programs are changing how enterprises operate, improving efficiency, reducing manual effort, and helping teams focus on higher-value work.
Instead of sitting on the sidelines as a separate tool, AI agents integrate directly into the flow of work. They plug into existing systems, understand context, take action, and even improve with use. And because they can be customized in plain English, they’re accessible to teams across the business—not just developers or data scientists.
As companies look for practical ways to apply AI, enterprise agents are delivering real results. From support and HR to finance and customer service, they’re showing up where the work happens and making it better.
What are enterprise AI agents?
AI agents are software programs that use artificial intelligence to complete tasks, often with little or no human input. Think of them as digital coworkers that can handle repetitive tasks, process data, and make decisions based on real-time signals.
In the enterprise, AI agents go beyond simple chatbots. They can:
- Interpret natural language
- Connect across tools and databases
- Automate multi-step workflows
- Learn and improve over time
- Operate within enterprise-grade security frameworks
They don’t require ripping and replacing existing systems. Instead, they sit on top of CRMs, ERPs, file storage, service management platforms, and more—gathering context, acting on data, and closing the loop.
And the best part? Business users can create and customize agents without relying entirely on IT. Using natural language runbooks, anyone can describe what they want the agent to do and watch it go to work.
That means faster time to value, more flexibility, and better alignment with how teams actually work.
What makes an enterprise AI agent effective?
Not all AI agents are created equal. For agents to drive real outcomes, they need to do more than respond to prompts. They need to think, act, and evolve in ways that align with how businesses operate.
Here’s what defines a strong enterprise AI agent:
1. Deep context
Enterprise agents need access to the full picture. That means pulling from both structured systems (like databases and ERPs) and unstructured content (like documents, Slack messages, and tickets). The more context an agent has, the better its decisions will be.
2. Multi-system integration
Most workflows touch multiple tools. AI agents should be able to move between them fluidly, reading a document in Google Drive, logging a task in Jira, pulling a customer record from Salesforce, and triggering actions in ServiceNow.
3. Autonomous reasoning
Strong agents don’t just follow steps. They reason through problems, choose next-best actions, and dynamically adapt to different scenarios. Glean calls this agentic reasoning: the ability for agents to operate more like humans—proactively, intelligently, and independently.
4. Security and governance
Enterprise agents must operate within the guardrails of IT. That means honoring permission models, encrypting data, maintaining audit logs, and aligning with compliance standards. Every action taken by an agent should be explainable and traceable.
5. Human oversight
Even the smartest agents need human input. Whether it's approving actions, providing feedback, or interpreting results, humans remain part of the loop—especially when it comes to sensitive data or complex decision-making.
Glean agents: Built for how enterprises work
At Glean, we’ve taken a different approach to AI agents. We’ve built them from the ground up to reflect how enterprises work, and how employees think.
Glean agents combine:
- Universal knowledge access: Connect across more than 100 tools, structured and unstructured data, and even real-time internet sources.
- Agentic reasoning: Autonomously execute multi-step tasks, analyze complex datasets, and make informed decisions.
- Natural language runbooks: Let business users define workflows in plain English—no coding required.
- Enterprise-ready architecture: Enforce strict permissions, support custom governance rules, and meet compliance standards.
These agents are fully integrated into the Glean Work AI platform, so they don’t operate in a vacuum. They build on everything Glean already knows about your business—who your users are, what they’re working on, and where knowledge lives.
The result? A faster, safer, and smarter way to bring AI to work.
Enterprise use cases: Where AI agents drive impact
AI agents aren’t theoretical; they’re already delivering measurable benefits in departments across the enterprise. Let’s look at how they’re transforming key functions:
IT and support
AI agents are changing the game for IT teams. They reduce ticket volume, speed up resolution times, and let support staff focus on more strategic work.
Common use cases include:
- Resolving issues like password resets, software access, and network troubleshooting automatically
- Pulling answers from internal knowledge bases and documents to respond to employee questions
- Routing tickets based on urgency, department, or historical patterns
- Detecting recurring issues and recommending long-term fixes
Glean agents can act on tickets in tools like Zendesk and ServiceNow, pull supporting context from Confluence or Google Drive, and even respond to employees directly in Slack or Microsoft Teams.
HR and employee experience
People teams are using AI agents to streamline operations and deliver a more consistent employee experience.
Key benefits include:
- Automating onboarding and offboarding flows
- Providing instant access to HR policies, forms, and benefit information
- Managing leave requests and PTO approvals
- Tailoring communications and resources to each employee’s role and location
AI agents reduce the burden on HR teams while making it easier for employees to get answers on their own—anytime, anywhere.
Finance and accounting
In finance, AI agents can automate routine operations while supporting more strategic analysis and planning.
They’re helping teams:
- Process invoices and match them to purchase orders
- Manage expense approvals and budget thresholds
- Coordinate with vendors and track payment status
- Run forecasts and analyze trends in financial data
Because they can access both structured systems and unstructured documents, agents help finance teams operate faster with less risk of error.
Sales and customer experience
Sales and CX teams are using AI agents to move faster, personalize engagement, and reduce admin work.
Common use cases:
- Following up with leads and booking meetings
- Updating CRMs with call notes or opportunity changes
- Personalizing responses to customer inquiries
- Surfacing upsell and renewal opportunities
And because agents can analyze feedback and sentiment, they help teams better understand customer needs and act on them faster.
How to get started with AI agents
If you’re ready to explore AI agents in your business, here’s a simple roadmap to follow:
1. Identify high-impact opportunities
Look for tasks or workflows that are:
- Repetitive and time-consuming
- Cross-functional or siloed across tools
- High in volume or error-prone
- Connected to business-critical outcomes
Start small, but think big. A single use case can unlock momentum across the company.
2. Choose the right platform
The platform you choose matters. Look for one that offers:
- Deep integration across your existing stack
- Robust reasoning and multi-step execution
- Support for non-technical users to create agents
- Enterprise-level security and data governance
Glean checks all these boxes and integrates seamlessly with the tools your teams already use.
3. Run pilot projects
Start with 1–2 pilots in departments that are eager for automation. Define clear metrics: time saved, resolution speed, error rates, or adoption.
Use early feedback to refine agents, improve training data, and identify broader opportunities.
4. Align on governance and oversight
Create policies for:
- Who can create or approve agents
- How agents are monitored and improved
- What data agents can access or modify
Balance autonomy with oversight so agents can move fast without risking trust.
5. Build internal readiness
AI agents are most effective when people understand how and why to use them. Educate teams, share wins, and show how agents make work easier.
The future of enterprise AI is agentic
We’re entering a new chapter in how people and software work together. AI agents aren’t just tools—they’re collaborators. And they’re only getting smarter.
Looking ahead, we expect to see:
- Inter-agent collaboration: Agents will work together, handing off tasks, sharing context, and coordinating across workflows.
- More personalization: Agents will adapt to each employee’s preferences, history, and habits.
- Smarter decision support: Beyond execution, agents will offer insights and recommendations grounded in data.
- Continuous improvement: As models improve and feedback loops grow, agents will evolve just like their human teammates.
A new way to think about work
Most AI conversations focus on speed, scale, or savings. But the real story with AI agents is subtler and more powerful.
They’re not just automating checklists. They’re stepping into the gray areas of work: coordinating across systems, responding in real time, and navigating messy, cross-functional tasks—the kind of work that doesn’t follow a script.
That shift, from rigid automation to contextual delegation, is what makes AI agents different. And it’s what makes them so valuable.
But intelligence alone isn’t enough. The agents that will actually move the needle are the ones with full context—agents that can draw from your systems, your knowledge, and your people’s workflows to make smart, trusted decisions.
That’s where Glean comes in. Our agents don’t operate in the dark. They see across your company’s data and apps, understand your teams' goals, and take action with confidence. They don’t just answer questions; they anticipate needs. And they’re built with enterprise guardrails from day one.
Ready to see it in action? Request a demo to explore how Glean agents can transform work across your company.
