AI agents are a leap forward from what most people know as generative AI, LLMs, or Chatbots. Agents can make decisions and take actions to achieve specific goals without constant human input. So, instead of waiting for your prompt, an AI agent can sense, decide, act, and learn on its own. In short, it’s not just chat. It’s teamwork.
Here’s an example: You’re trying to improve customer retention across dozens of markets. You have plenty of data, but you’re drowning in it. An AI agent doesn’t just summarize your dashboards. It notices churn patterns, chooses the best retention strategy, and launches a campaign without explicitly asking for specific metrics.
How do Agentic systems actually work?
Agentic AI isn’t a single technology. It’s a system made up of familiar parts but wired together to do something new. Let’s break down the four core capabilities:
- Perceive: Ingest and interpret data—from emails to IoT sensors to CRM records.
- Decide: Reason through options based on context, memory, and goals.
- Act: Trigger workflows, update databases, send alerts, even file reports.
- Learn: Improve over time based on feedback loops and observed outcomes.
This is what moves AI from automation to autonomy.
What powers an AI agent?
AI agents can be powered by large language models (LLMs), but they don’t have to be. At their core, agents are systems composed of coordinated components —LLMs, rule-based engines, symbolic reasoning systems, or other forms of intelligence that work together to perceive, decide, act, and learn in pursuit of specific goals.
- System prompts shape the agent’s personality and rules
- Memory stores session info and long-term knowledge
- Reasoning engines break down goals into actionable steps
- Tools connect it to your business systems (APIs, databases, code libraries)
- Interfaces define how users interact — via chat, voice, or embedded apps
In enterprise environments, all of this is standardized by MCP servers (Model Context Protocol). MCPs define how agents access, reference, and maintain consistency in external context across tasks and sessions. MCPs enable multiple agents and tools to coordinate by sharing context through structured, governed protocol — ensuring reliable, enterprise-grade behavior.
Where can I use AI agents in my business?
Start with real needs. AI agents are already helping enterprises clean up messy survey data from global teams by standardizing formats and enriching entries with geolocation or currency data. In retail, they are proactively preventing churn by monitoring early disengagement signals and launching retention strategies automatically. And in finance, agents are securely accessing workflows to fetch KPIs, run analyses, and deliver tailored insights on demand.
This is where the Alteryx AI Data Clearinghouse becomes a critical enabler. Acting as a governed, vendor-neutral intermediary layer, the Clearinghouse orchestrates trusted, contextualized data flows between disparate systems and any LLM or AI application.
It ensures the data your agents use is relevant, auditable, and ready for action — mitigating the risk of hallucinations or poor decisions. Whether you’re routing insights to OpenAI, Anthropic, or Gemini, the AI Data Clearinghouse provides the connective tissue that turns fragmented data into trustworthy, AI-ready assets.
What makes agentic AI trustworthy in enterprise environments?
It all comes down to architecture. Governance-first design ensures every action flows through MCP protocols, giving you visibility and control. Scoped autonomy lets you define the boundaries within which agents operate. With full traceability, every step is logged and explainable. And because agents can connect to any LLM or system, you’re never locked into one vendor. It’s not just smart AI. It’s compliant, transparent AI that works within your business rules.
Can I use AI agents with Alteryx today?
These capabilities are currently in development and available to select users by request. But the foundation is already live: Alteryx enables visual orchestration of agentic workflows, combining intuitive user interfaces with robust security.
With Alteryx, you can build agents that perceive messy data, reason through decisions using historical context, take action across integrated systems, and improve continuously. And with MCP integration, these agents can easily collaborate with external systems and tools.
What should I do now?
Start by identifying the sticking points. What repetitive decisions drain your team’s time? Where does your data sit idle, waiting for interpretation? If you had a trusted digital teammate, what would you offload first? Because agentic AI isn’t hypothetical. It’s here. And it’s ready to make a difference as soon as you are.
Interested in learning more about how Alteryx can support your AI initiatives? Contact us today.
*Editor’s Note: The content of this blog was based on information from the webinar “Your First Step into Agentic AI” presented by Alteryx and Slalom Consulting.