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    What Are Agents?

    Agents are AI-based systems that can complete tasks, make decisions, and interact with software or data sources with limited human guidance. Unlike traditional automation tools that follow the same predefined process every time, agents can adapt to changing conditions and respond based on context.

    Expanded Definition

    An agent acts as a bridge between data and decision-making. It can gather information from connected systems, evaluate evolving situations, and trigger the next step in a process. Some agents rely on structured business rules, while others use machine learning or generative AI to adapt over time.

    Agents are commonly connected to analytics platforms, enterprise systems, and cloud-based applications. This flexibility makes them useful in organizations that manage large amounts of business or customer data. BCG has also noted growing enterprise interest in AI agents that can coordinate actions across business systems rather than simply assisting with isolated tasks.

    Enterprise adoption of AI agents continues to expand as companies look for practical ways to automate repetitive work and improve responsiveness across the business. Gartner estimates that 40% of enterprise applications will include task-specific AI agents in 2026, up from less than 5% in 2025, while Forrester has said enterprise software is evolving to support “a digital workforce of AI agents” rather than relying solely on human-driven workflows. BCG has also noted that many organizations are shifting from AI experimentation toward operational agentic AI initiatives designed to scale automation across the business.

    How Agents Are Applied in Business & Data

    Teams often use agents to reduce manual effort and react more quickly to operational shifts. They’re especially useful in environments where employees work across multiple systems or vast quantities of operational data.

    Customer support teams might use agents to monitor incoming service requests and escalate urgent issues automatically. In finance, agents can help flag unusual spending activity before reporting cycles begin. Supply chain teams often rely on agents to monitor inventory conditions continuously so they can address disruptions earlier.

    Within analytics environments, agents help automate reporting workflows and monitor business activity in real time. They can also trigger follow-up actions when specific thresholds are reached, helping teams respond more quickly with less hands-on oversight.

    Alteryx supports these workflows through analytics automation and AI-led orchestration capabilities. Teams can use Alteryx to prepare data as well as automate and scale business processes across cloud and enterprise environments with less manual intervention.

    Agents tend to deliver the most value in environments with repetitive workflows or rapidly changing conditions. They can help teams stay ahead of operational issues and reduce slowdowns that come from manual processes or fragmented workflows.

    Common applications include:

    • Real-time monitoring: Monitor operational activity continuously so teams can identify issues earlier and respond before disruptions begin affecting business performance
    • Workflow automation: Reduce the time spent coordinating repetitive processes across systems by allowing agents to handle routine operational tasks automatically
    • Data-intensive operations: Spend less time gathering and organizing information so employees can focus more attention on analysis, planning, and decision-making
    • Customer-facing experiences: Help service teams respond faster by prioritizing requests automatically and giving employees quicker access to relevant customer information

    Successful deployments also depend on strong governance and integration planning. Gartner estimates that more than 40% of agentic AI projects will be canceled by the end of 2027 because of challenges tied to business value, costs, and risk management, reinforcing the need for strong oversight as agent adoption grows.

    How an Agent Works

    Agents operate by combining data access with workflow logic so actions can happen automatically under shifting circumstances.

    While implementations vary, most agents follow a similar process that helps systems respond more intelligently to business activity:

    1. A trigger starts the process. An agent may respond to a user request, a scheduled task, or a change in business conditions such as a system alert or inventory issue.
    2. The agent collects relevant information. To assess the current situation, data is gathered from connected applications, analytics platforms, or operational systems.
    3. The agent evaluates what should happen next. Based on predefined logic or AI-enabled reasoning, the agent determines the most appropriate next step.
    4. An action is executed automatically. This may involve generating a report or triggering an action within a connected system.
    5. Results are monitored over time. Some agents continue tracking outcomes after an action occurs so processes can be refined for better performance.

    Challenges of deploying agents

    Although agents can save time and improve efficiency, rolling them out across real business environments is not always straightforward. Many organizations are still figuring out how to scale agent usage responsibly while maintaining visibility, control, and reliable performance across systems.

    Common challenges include:

    • Data quality: Agents rely on accurate and up-to-date information to generate useful outputs and make reliable decisions
    • System integration: Connecting agents across cloud platforms, enterprise applications, and analytics tools can become complicated as environments grow
    • Governance and oversight: Teams often need clear guidelines around accountability, monitoring, and when human involvement should remain part of the process
    • Security and access management: Agents may require broad access to business systems, making permissions and compliance controls especially important
    • Business alignment: Some organizations struggle to move beyond experimentation and identify use cases that deliver measurable business value

    Agents tend to work best in environments where processes are already well-organized and teams have strong visibility into daily operations. As adoption becomes more widespread, many organizations are also spending more time evaluating governance practices, data quality standards, and long-term reliability before expanding agent usage further.

    Use Cases

    Many teams use agents to reduce repetitive work and improve how day-to-day processes are managed across the business. Agents are especially effective in environments where teams manage fast-moving workflows across multiple systems.

    Here are some examples of how different business functions use agents:

    • Finance: Accelerate reconciliations and identify unusual transactions earlier in the reporting process
    • Marketing: Monitor campaign performance and adjust targeting strategies based on customer behavior and engagement trends
    • Operations: Identify workflow bottlenecks more quickly when systems detect performance issues and trigger the next steps automatically
    • Supply chain: Track inventory movement and fulfillment activity in real time to reduce delays across distribution networks
    • Customer support: Prioritize incoming requests automatically and provide teams with relevant customer context to improve response times

    Industry Examples

    Agents are helping industries improve responsiveness and manage operational workflows more effectively in data-rich environments:

    • Healthcare: Reduce administrative delays by automating claims reviews and improving patient scheduling workflows
    • Retail: Respond faster to demand shifts by monitoring inventory trends and customer purchasing patterns
    • Manufacturing: Minimize downtime by identifying equipment risks earlier and responding to maintenance issues before disruptions escalate
    • Telecommunications: Improve network reliability by detecting performance issues quickly and responding to outages before they affect larger customer groups

    FAQs

    What’s the difference between an AI agent and automation? Traditional automation follows predefined rules and repeats the same workflow each time a process runs. AI agents are more flexible because they can interpret context and adjust their actions when conditions change. This capability allows organizations to automate processes that require more dynamic decision-making.

    Are agents the same as chatbots? Chatbots are primarily designed to answer questions and support conversations with users. Agents go further by interacting with business systems and completing tasks on a user’s behalf. In many cases, agents can manage workflows with minimal human involvement.

    Can agents work with analytics platforms? Agents are often connected to analytics platforms so teams can automate reporting workflows and respond to changing business conditions more quickly. Within the Alteryx platform, agents can help trigger workflows and support analytics automation based on real-time data activity. This allows teams to act on insights faster without relying as heavily on manual processes.

    Do agents require machine learning? Many agents work effectively using rules and structured workflows alone. More advanced agents may incorporate machine learning or generative AI to adapt to changing conditions and support more sophisticated decision-making over time.

    What are the benefits of using agents? Agents help organizations reduce manual work and respond more quickly to changing business conditions by automating repetitive processes and supporting faster decision-making. They can improve operational efficiency across teams while helping employees spend less time managing routine tasks and more time focusing on strategic work that benefits from human expertise and judgment.

    Further Resources

    Analyst Report | Unlocking Agentic AI: The Case for Unified Data Platforms

    Webinar | Your First Step into Agentic AI

    Blog | What is an AI Agent? Meet Your Digital Teammate

    Webinar | Push Past the Basics: See What Alteryx Copilot Can Really Do

    Sources and References

    Forrester | Predictions 2026: AI Agents, Changing Business Models, And Workplace Culture Impact Enterprise Software

    Gartner | Gartner Predicts 40% of Enterprise Apps Will Feature Task-Specific AI Agents by 2026, Up from Less Than 5% in 2025

    BCG | The $200 Billion Agentic AI Opportunity for Tech Service Providers

    Gartner | Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027

    BCG | AI Agents

    Synonyms

    • Intelligent agents
    • Autonomous agents
    • AI assistants
    • Software agents
    • Digital agents

    Related Terms

    Last Reviewed:

    June 2026

    Alteryx Editorial Standards and Review

    This glossary entry was created and reviewed by the Alteryx content team for clarity, accuracy, and alignment with our expertise in data analytics automation.