What Are AI Agents?
Ross Sylvester, Co-Founder & CEO, Adrata | Feb 2025 | ~5 min read
Everyone's talking about AI agents. Most explanations are confusing, overly technical, or filled with hype. Here's a clear, practical explanation of what AI agents actually are and why they matter.
The Simple Definition
An AI agent is software that can take actions on your behalf to accomplish a goal.
That's it. Unlike a chatbot that just answers questions, an agent can:
- Use tools (search the web, query databases, call APIs)
- Make decisions about what to do next
- Take multiple steps to complete a task
- Learn from the results of its actions
The Key Difference
Chatbot: You ask a question, it gives an answer. Agent: You give a goal, it figures out the steps and executes them.
How Agents Work
Every AI agent follows the same basic loop:
Observe -> Think -> Act -> Repeat
- Observe: The agent receives input (a goal, context, or feedback from a previous action)
- Think: The LLM reasons about what to do next
- Act: The agent uses a tool or produces output
- Repeat: The cycle continues until the goal is achieved
Example: Lead Research Agent
Let's say you give an agent the goal: "Research Acme Corp for a sales call."
- Think: "I need to find basic company info first."
- Act: Searches the web for "Acme Corp company information"
- Observe: Gets search results showing Acme Corp is a $50M manufacturing company
- Think: "Now I should look for recent news and hiring patterns."
- Act: Searches for "Acme Corp news 2025" and "Acme Corp jobs"
- Observe: Finds they just raised funding and are hiring sales people
- Think: "I have enough information to create a briefing."
- Act: Produces a structured research document
Agents vs. Other AI
Chatbots:
- Answer questions
- Single-turn interactions
- No external actions
- Stateless
AI Agents:
- Accomplish goals
- Multi-step workflows
- Use tools and APIs
- Maintain context
Automation (RPA):
- Follow fixed scripts
- Break on edge cases
- No reasoning
- Brittle
AI Agents:
- Adapt to situations
- Handle edge cases
- Reason about problems
- Flexible
The Three Types of Agents
1. Single-Purpose Agents
Do one thing really well. Examples:
- Lead research agent
- Email drafting agent
- Meeting prep agent
- Data enrichment agent
2. Workflow Agents
Orchestrate multi-step processes with predefined paths. Examples:
- Lead qualification, then research, then outreach sequence
- Demo scheduled, then prep materials, then follow-up
- Deal closed, then onboarding, then handoff
3. Autonomous Agents
Operate with minimal human guidance, making complex decisions. Examples:
- SDR agents that handle entire outbound sequences
- Research agents that continuously monitor accounts
- Pipeline agents that proactively surface insights
Why Agents Matter for Sales
Sales is uniquely suited for AI agents because:
- High-volume, repetitive tasks: Research, data entry, follow-ups
- Clear success metrics: Meetings booked, deals closed
- Tool-heavy workflows: CRM, email, calendar, enrichment
- Time-sensitive: Speed matters, agents work 24/7
"The average sales rep spends 65% of their time on non-selling activities. Agents can eliminate most of that."
What Agents Can't Do (Yet)
Be realistic about current limitations:
- Build relationships: Agents can prep you, but you build rapport
- Negotiate: Complex multi-party negotiations need humans
- Read the room: Emotional intelligence in live calls
- Handle novel situations: Truly unprecedented edge cases
- Be accountable: When it matters, a human is on the hook
Getting Started
If you're new to agents, start here:
- Identify one task that takes 10+ minutes and is mostly repetitive
- Map the steps a human would take to complete it
- Identify the tools needed (search, database, email, etc.)
- Build or buy an agent to handle it
- Measure the results and iterate
Start with something low-risk and high-frequency. Lead research is a great first agent -- it saves time on every lead and mistakes aren't catastrophic.
Next Steps: Ready to build? Check out Build Your First AI Agent for a step-by-step guide.