What is an AI Agent?

How Agentic AI Works: Building Deterministic Agents You Can Actually Trust

We’ve all heard about agentic AI for a while now. But what really is an AI agent? With the speed of AI innovation right now, it can feel a bit overwhelming or scary if you’re trying to figure it all out. 

An AI agent is pretty much like a coworker: you’ll mostly only want to continuously work with them if they help you achieve your goals or your company’s goals better and faster. And if they treat you right, of course. 

Creating an agent that predictably creates value for you and your team requires a few key components. Let’s break them down. 

Let’s Go Under the Hood of Your Favorite Agent

At a high level, you can divide an agent into two big parts: 

1. The agent definition:

• How it should behave

• What tasks it should be able to perform

• What abilities it has

2. And the agent’s context: 

• Which information it should have in order to make informed decisions

• How it will help the team with the challenges at hand

How Do You Define an AI Agent? 8 Critical Abilities

First, we need to tell the agent what to do and give it abilities. This means:

1. Instructions

A clear set of instructions (e.g., a good prompt) is key to avoid garbage in, garbage out. That’s a critical problem: not all your subject matter experts (SMEs) know how to whisper in the ear of an AI system. 

This is key: for maybe the first time, all subject matter experts, across all functions in your company, can be builders.

2. Skills

Skills are detailed instructions given to agents to be able to achieve a repetitive task reliably and consistently. Just like you, an agent will need to be coached to hone its skill set at particular tasks. 

Here’s an example: Say that you want to get help from your agent on building your next slide deck. At a high level, building a slide deck often consists of:

• Loading your company’s template so your content fits your branding guidelines

• Restructuring the information you want to convey in a way that is conducive to being consumed on a slide (shorter sentences, hooks, quotes, images)

• “Molding”  that content into the template slides you have: creating a title slide, an agenda slide, comparison tables, charts, Q&A slides, etc

There’s a good chance that every company will have slightly different policies to create these slides, but once you’ve learned them, you’ll repeat this process regularly. 

In AgentSpot, we took a slightly different approach to skills. Skills can be shared across your entire organization, across multiple agents. 

This means all your agents can leverage the coaching being done by your SMEs. User stories skills, slide-building skills, legal research skills, and root cause analysis skills are available to all users across agents. 

This allows you to easily level up the effectiveness of your agentic workflows. And most importantly, of your entire team.

3. Tools

Think of tools as superpowers. Do you want your agent to be able to search the web for external information, or would you rather bind it to internal resources? Do you want it to create charts or images? 

When creating agents, it is equally important to define what they cannot do as it is to define what they can do. You will want a trusted, bounded agent in your team: not an agent that doesn’t know which ability it should use and when. 

Giving clear instructions and only the most necessary skills and tools it needs will maximize your chances of exhibiting predictable behavior and addressing the tasks with the consistency and quality you expect of an expert.

4. Agent Context

You’ve probably heard this before: AI needs context. It needs information to understand the situation at hand, how similar things were handled in the past, and data to make informed decisions. 

5. Files

You may have information, instructions, or even examples of past outcomes that you want your agent to reference as it reasons through its task. 

These files will usually be spreadsheets, text documents, slide decks, PDFs that contain information that this agent should be aware of to gain specific knowledge, or to see exactly what the expected output should look like.

6. Connectivity to Apps

Many of the things that happen at work will result in an output in a cloud application. A conversation in Slack; a ticket in JIRA, Salesforce, or Asana; a document stored in Google Drive, etc. 

You want your agent to be able to retrieve that information to get more context on its environment and, better yet, you may even want your agent to take action for you in these systems so you don’t have to. 

Connectivity to the systems you use to do your work is key for your agent to help you effectively. 

In AgentSpot, we use the MCP protocol for the vast majority of our connectors for multiple reasons: it lets you control what exactly the connector should be able to do in your organization via administrative tool control. 

7. Connectivity to Data 

This is an understanding of your key performance indicators, but more importantly, of how to use them to solve larger questions you may have. 

For example, if you ask “Which levers do I have to improve profitability next quarter?”, a deep understanding of why things happen and what is likely to happen next will ground your agent in facts. 

This helps it make or recommend a better decision for you. AgentSpot stands on the shoulders of ThoughtSpot’s Spotter, a leading agent for analytics, which will be able to reason like your best analyst and gather insights and recommended actions stemming from ThoughtSpot’s rich semantic layer.

8. Memory

If you think this wasn’t cool stuff before - this is where it gets interesting. Agents are like the North: they remember. They remember your own preferences, or small instructions you gave them while doing a task. 

The goal is for you to avoid having to repeat yourself, and to create a virtuous cycle where the more you work with your agent-peer, the more efficient you get.

But that’s not all. In a company, you don’t want key information to be siloed, and you don't want each of your employees to learn through isolated experiences. 

Some facts and methods should be a plinth on which everyone in your company stands to build in the same direction. The same applies to an agentic platform. 

Agent memory starts to really compound when it works in layers: 

1. The organizational memory layer: This is where the core knowledge and methods reside that all your agents tap into, so you don’t have to keep re-explaining yourself for any new agent you or your team builds. 

2. The personal memory layer: Here, your preferences (brief, direct sentences with data given using the metric system for source citation and reasoning) are taken into account to ensure all your agents serve you well.

3. The agent memory layer: As all your collaborators use it, it learns from everyone… And everyone benefits.

What Do AI Agents Actually Do for You?

We’ve seen the expression “agents” so much at this point that we may forget that at the core of AI agents is agency. Agents are AI systems that can reason and act. 

Gone are the days when you could only chat with a large language model to have it guide you (or at least, constantly reassure you that you are always right) with a problem you’re trying to solve. 

Agentic AI can now either take tasks completely off your plate, or at least be delegated part of your work and act to concretely help you get it done. 

That’s where app connectors will shine again: through the MCP protocol (and, in large organizations, usually governed by your admins), agents can take actions for you. From our experience with AgentSpot, we found that users typically use agents for the following tasks:

But these are just examples! Your imagination is the limit here. That, and MCP tools from your favorite apps, which are currently being enriched at breakneck speed.

5 Tips and Tricks to Take Advantage of an Agentic World

1. Always Iterate

AI Agents should be tested. Have a bias for action. A good agentic platform should make it easy for you to create an agent to help you with a specific problem. 

Go ahead and create an agent, save it, and ask it questions, from easy ones to harder and harder ones. Test it! As you see it fail, go back in a building conversation to improve the agent and make it fit your needs. 

This is critical not just at the beginning of agent creation, but continuously. As time passes, your expectations of the agent may evolve. Invest in teaching it how it can help you best.

2. Work as a Team

There’s nothing more satisfying than helping others, and yes, chances are that there is “an agent for that!”

Sharing agents across your team ensures more deterministic methods across your organization (no shadow AI) and ensures that the right person (the SME) builds the agent for the rest of the team to leverage their specific expertise. 

As others use a shared agent, they’ll have great feedback for the builder and can even help the builder improve that new teammate.

3. Use Agents as Your Glue

It’s never been easier for a non-technical person to have all of their different repositories of work talk to one another. AI agents can be the glue that helps you retrieve and act on information disseminated across multiple systems. 

They’re amazing at gathering large amounts of information. Note down what apps you work with each week, and what you have to do manually today that takes a significant amount of time.

If you can explain it, you can now create an agent for it to accelerate, if not entirely automate, all of this work.

4. Bring Agents Where Your Team Lives

Foster communication and collaboration. In AgentSpot, for instance, you can allocate specific agents to specific Slack channels. Have your CSM agent help your entire Customer Success team get more context on specific accounts in the #customer-outcomes channel. 

Invoking an agent in #prospects helps your pipeline generation agent focus your team on the right opportunities and equip them with the right messaging and context handling for every single account you’re going after.

5. Create Virtuous Cycles

The best agents in our team are agents that build what we call virtuous cycles. For instance, create knowledge files as part of each conversation, so that the output of past research from anyone in your team is available not only to that agent in future runs, keeping its context up to date, or enabling it to tell you what changed since you last spoke to it. 

But also to people - for example, a repository of best practices that gets built one conversation at a time. This means knowledge can be shared and reviewed across your organization, and it helps with documentation problems that often come up when scaling a business. 

Build Your Next Agent in AgentSpot

You now know what an AI agent is, how it works, and what it can do for you. But agents are just the tip of the iceberg. 

AgentSpot doesn't limit you to conversations. We know different tasks need different tools, so the platform separates agents from apps and workflows. All three give you agency, just in different ways with different benefits. 

Your first three agents are free - start building your agents here.

P.S. We’re pretty sure you don’t need our help, but we’ll also build them with you if you want the company!