What are AI agents?

Summary

• AI agents are autonomous software systems that perceive, reason, act, and remember, enabling them to complete multi-step tasks that traditional chatbots and rule-based automation cannot handle.
• Enterprise adoption is accelerating rapidly, but organizations must address agent sprawl, governance, evaluation, and cost control to move successfully from prototype to production.
• Agent Bricks on the Databricks Platform provides a unified control plane for building, running, and governing AI agents across any model, provider, or framework with built-in evaluation and self-improving capabilities.

What are AI agents?

AI agents are software systems that reason, plan, and act autonomously to complete multi-step tasks. Unlike simple automation scripts or rule-based bots, they perceive their environment, make decisions, and take actions with minimal human oversight. According to IBM, an AI agent is "a system or program that is capable of autonomously performing tasks on behalf of a user or another system."
As organizations adopt AI agents at scale, new challenges emerge. Fast adoption creates agent sprawl-the accumulation of different models, clouds, and frameworks producing a complex, ungoverned environment. Leaders struggle to answer basic questions: "Which agents exist?", "What data do they access?", and "How well do they work?" A recent report on the state of AI agents highlights these growing concerns across industries.

How do AI agents work?

AI agents operate through a continuous loop of four core components:

• Perception: Collects data from APIs, databases, user inputs, and other environmental sources.
• Reasoning: Uses a large language model (LLM) to interpret context and decide next steps.
• Action: Executes tasks such as calling tools, querying data, or triggering workflows.
• Memory: Retains information across interactions to improve future decisions.

As Google Cloud notes, AI agents "show reasoning, planning, and memory." This loop lets agents handle multi-step processes that traditional automation cannot.

What types of AI agents exist?

AI agents range from simple to highly autonomous. Common architectures include:

Type	Description
Reactive agents	Respond to inputs with predefined rules; no memory
Goal-based agents	Plan actions toward a defined objective
Learning agents	Improve over time using feedback and evaluation
Multi-agent systems	Coordinate multiple specialized agents on a shared task

MIT Sloan defines agentic AI as "systems that incorporate multiple, different agents that are orchestrating a task together." Multi-agent systems are increasingly common in enterprise settings where complex workflows span multiple domains.

AI agents vs. traditional chatbots

Understanding the distinction helps teams choose the right approach:

• Chatbots follow scripted conversation flows and respond to predefined intents. They work well for FAQs and simple lookups.
• AI agents autonomously plan, use tools, and take multi-step actions. They break a goal into subtasks, call external APIs, and adjust their approach based on results.

The key difference is autonomy. A chatbot answers a question; an agent completes a task.

Why AI agents matter for business

According to Gartner, 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from less than 5% today. Yet Gartner also predicts that more than 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.
Real-world use cases include:

• Customer service: Personalized concierge experiences that help customers find products or resolve issues.
• Supply chain: Forecasting demand at individual stores and automating inventory replenishment.
• Financial services: Reviewing loan applications and applying underwriting policies.
• Life sciences and retail: Extracting structured information from unstructured data at scale.

The challenge is moving from prototype to production while maintaining governance, accuracy, and cost control. See how enterprise leaders are scaling AI agents across their organizations.

Best practices for deploying AI agents

Organizations should consider these principles before going to production:

1. Start with a clear business objective. Define measurable outcomes before choosing models or frameworks.
2. Establish governance early. Track which agents exist, what data they access, and who owns them. Governing AI agents at scale is critical for maintaining control.
3. Build evaluation into the workflow. Benchmark agent outputs against ground-truth data continuously.
4. Plan for multi-model flexibility. Avoid lock-in by designing architectures that can swap models as capabilities evolve.
5. Implement guardrails. Set safety controls, cost limits, and escalation paths for edge cases.

How Agent Bricks addresses agent sprawl

For organizations standardizing on the Databricks Platform, Agent Bricks (Mosaic AI Agent Framework) provides a unified control plane for building, running, and governing AI agents across any model, provider, or framework.

• Open and governed: Build with any AI model-OpenAI, Gemini, Llama, Anthropic-and any framework while maintaining granular access controls, lineage tracking, cost controls, and policy enforcement.
• Contextual reasoning: Agents gain semantic understanding of enterprise data through learned business context, producing high-accuracy outcomes for document retrieval and processing.
• Self-improving: Built-in evaluation loops, prompt optimization, fine-tuning, and human feedback (RLHF) automatically improve performance so agents stay accurate without costly rebuilds.

Other tools in the ecosystem include Azure AI Foundry Agent Service, Amazon Bedrock Agents, Vertex AI Agent Builder, Salesforce Agentforce, and OpenAI Agents SDK. The right choice depends on your existing infrastructure, governance requirements, and model preferences.

FAQs

How do AI agents work and what are their core components?

AI agents work through a continuous loop of perception, reasoning, action, and memory. They collect environmental data, use an LLM to interpret context and plan, execute tasks via tools or APIs, and retain information for future interactions.

What are the different types of AI agents and their architectures?

Common types include reactive agents, goal-based agents, learning agents, and multi-agent systems. Architectures range from simple stimulus-response designs to complex orchestration patterns where multiple specialized agents collaborate.

What is the difference between AI agents and traditional chatbots?

AI agents autonomously plan, use tools, and take multi-step actions, while traditional chatbots follow scripted conversation flows. As Google Cloud explains, agents demonstrate reasoning, planning, and memory-capabilities rule-based chatbots lack.

What are common real-world use cases for AI agents in business?

AI agents power customer service concierges, demand forecasting, document extraction, and workflow orchestration. Financial services, retail, life sciences, and manufacturing are leading adoption. For a healthcare example, see how the Care Cost Compass agent system was built using Mosaic AI Agent Framework.

How do AI agents use large language models to make decisions?

LLMs serve as the reasoning engine. The agent passes context-user input, retrieved data, tool outputs-to the LLM, which generates a plan or action. The agent executes that action and feeds results back into the loop.

What are autonomous AI agents?

Autonomous AI agents interact with their environment, collect data, and perform self-directed tasks, as described by AWS. They continuously evaluate outcomes against goals and adjust behavior accordingly. Learn more about how to build an autonomous AI assistant with Mosaic AI Agent Framework.

What tools and frameworks are used to build AI agents?

Options include Agent Bricks on the Databricks Platform, Azure AI Foundry Agent Service, Amazon Bedrock Agents, Vertex AI Agent Builder, Salesforce Agentforce, and the OpenAI Agents SDK. Selection depends on governance needs, model preferences, and existing infrastructure.

How do multi-agent systems work?

Multi-agent systems coordinate specialized agents to handle complex tasks. Each agent focuses on a subtask, and an orchestrator routes work between them. This pattern improves reliability and enables parallel processing.

What are the risks of deploying AI agents in production?

Key risks include hallucinations, security vulnerabilities, uncontrolled costs, and lack of governance. Gartner warns that "agentic AI will lead to unwanted outcomes if it is not controlled with the right guardrails."

How do AI agents handle memory, planning, and reasoning?

Agents use memory to retain context across interactions, planning modules to break goals into subtasks, and LLM-based reasoning to decide which tools or actions to invoke at each step.
Ready to build, deploy, and govern AI agents at scale? Explore Agent Bricks on the Databricks Platform to get started.