Want to build an AI Agent? Let's understand the basics first.
Here's the core components that make an AI Agent - explained in simple English.
In the last post, we broke down what AI agents are:
Not just prompt-followers. Not just tool-wrappers.
But systems that can plan, act, reflect, and adapt… all toward a goal.
That was the what. Now let’s talk about the how.
Because while AI agents sound magical in theory, they’re still surprisingly fragile in practice.
Building one that’s reliable, useful, and not constantly breaking?
That’s a whole different game.
In this post, we’ll break down what goes into making a good AI agent…. and what separates toy demos from real-world systems.
🧠 The Core Components of an AI Agent
At a high level, every agent is made up of three things:
1. A Model (The Brain)
This is usually your LLM: like GPT-4, Claude, or Mixtral. It handles reasoning, decision-making, and task planning.
But the model alone isn’t the agent. It’s just the thinker.
2. A Set of Tools (The Hands)
These are the APIs, data sources, and functions the agent can use. They allow the agent to act… whether that’s fetching info, updating a dashboard, or sending an email.
This is where MCP or plugin systems come in. They let the model “touch” the outside world.
3. Instructions or System Prompts (The Playbook)
This includes the agent’s goals, constraints, behavior style, memory structure, and available tools. It sets the boundaries for what the agent is trying to do, and how it should go about it.
Together, these form the minimal viable anatomy of an agent.
⚙️ The 4 Core Patterns of Agentic Behavior
Most useful AI agents follow one (or more) of these behavioral patterns.
These patterns are what make an agent more than just a fancy wrapper around ChatGPT.
1. Planning Pattern
The agent decomposes a big goal into smaller steps.
Instead of just answering a prompt, it maps out a strategy.
Task: “Summarize this report and email stakeholders.”
The agent might break that into:
Parse report
Generate summary
Draft email
Send via SMTP
Planning is what makes the agent feel intentional (not reactive).
2. Tool-Use Pattern
The agent chooses the right external tool for each step.
Think of it as the agent’s “muscle memory.”
“Where’s my order?”
→ Query order database
→ Fetch tracking link
→ Draft message for customer
This is where the MCP protocol and tool access come in.
Tools turn language models into action models.
3. Reflection Pattern
The agent evaluates its own outputs. Did the plan work? Did the response make sense? Does the data look right?
If not, it tries again… or adjusts course.
This reflection loop is what makes the agent self-correcting.
Without it, agents tend to hallucinate confidently and move on.
4. Multi-Agent Pattern
Instead of one agent doing everything, multiple agents specialize and collaborate.
Think:
A planner agent breaks down the task
A researcher agent gathers info
A writer agent generates content
A QA agent reviews the final output
Multi-agent systems mimic human teams (but require more orchestration logic).
⚖️ When Should You Use an Agent (and When You Shouldn’t)
Just because you can use an agent doesn’t mean you should.
✅ Use agents when:
The task is multi-step or ambiguous
The path to success depends on judgment
The agent may need to retry or adapt mid-task
Examples:
“Investigate why sales dropped last week.”
“Draft a weekly ops report based on CRM + Notion + Slack threads.”
❌ Use simpler flows when:
The task is predictable and repetitive
You don’t need reasoning… just execution
The margin for error is very low
Example:
“Send this email every Monday at 9am.”
In short: agents are great at handling uncertainty, flexibility, and goal-seeking.
If you just want a script, don’t overengineer.
🧱 What Goes Into Building a Real Agent (Not Just a Demo)
A solid agent has more than a model + a plugin. Here are the extra layers that matter:
🧠 Memory
Short-term: What happened in this current task?
Long-term: What has this agent done before?
Memory lets agents build context over time, reference past work, and reduce repeated errors.
🔍 Retrieval
RAG (Retrieval-Augmented Generation) gives the model access to up-to-date, relevant information (from files, APIs, or vector databases).
This is essential when the task involves reasoning over private or external data.
🛠️ Tool Integration
Agents must know:
What tools are available
How to use them
When they should not be used (guardrails)
This is where MCP shines… giving models structured access to tool capabilities with reusable templates.
🔄 Orchestration Logic
Good agents need a decision loop:
What’s my goal → What’s the next best step → What tool do I need → Did it work → What now?
Sometimes you build this as a hardcoded loop. Sometimes you delegate to the model via meta-prompts.
Frameworks like AutoGen, LangGraph, and CrewAI help you manage this orchestration layer.
🚧 Common Failure Modes (And How to Catch Them Early)
Even smart agents mess up, and often in ways that feel oddly human.
They can get stuck in loops, repeating the same step endlessly. Or they might misuse tools, like updating the wrong field or deleting instead of fetching. Some forget their main goal, veering into side tasks. Others suffer from overconfidence, claiming success before anything’s actually done.
The fix? Build in resilience.
Use guardrails to limit risky actions. Add verification steps where the agent checks its own output:
“Does this result match the task?”
And when in doubt, keep a human-in-the-loop… especially for irreversible steps like sending emails or making updates.
You’re not building a perfect agent. You’re building one that knows how to recover.
🛠️ How to Actually Start Building One
You don’t need to be a developer to start thinking agentically.
You just need the right tools (and a simple task).
Here’s a low-friction way to begin:
Step 1: Pick a small, repetitive task you do often
Think:
“Summarize meeting notes and email the team”
“Check a Notion page and flag overdue tasks”
“Pull CRM data and draft a weekly update”
This will be your agent’s job.
Step 2: Use a no-code platform that supports agents
Try tools like:
OpenAI GPTs (you can build custom GPTs with tool access)
Flowise (visual interface to connect LLMs to tools)
Zapier + ChatGPT plugin (for light automation with context)
CrewAI or Superagent (for guided, no-code agent creation… better if you’re experimenting)
Pick one, sign up, and explore templates. They’re all known for being easy-to-use no-code tools and platforms.
Step 3: Give it a clear set of instructions
This is where the magic happens.
You’ll write a system prompt… a few lines that define your agent’s role.
Example:
"You are a helpful operations assistant. Every Monday at 10am, check this Notion board, summarize overdue items, and send a Slack message to the #ops channel."
If you’ve read my Prompt Engineering 101 post, you know that you need to keep it:
Clear (what’s the goal?)
Contextual (where’s the info?)
Actionable (what should the agent do with it?)
Step 4: Add tool access i.e. the agent’s “hands”
Most platforms will let you connect tools like:
Google Calendar or Gmail
Notion, Slack, or Trello
Web search or file readers
This is where MCP-style thinking comes in: you’re plugging the model into systems it can act on.
Pick 1–2 tools first. Get those working before adding more.
Step 5: Run, Watch, Fix, Repeat
Run the agent. See what it outputs.
Ask:
Did it understand the task?
Did it pull the right info?
Did it take the right actions?
If not, tweak the prompt or tool config.
Add a “reflection” step like:
“After every run, summarize what went well and what could be improved.”
You’re not just building a task bot.
You’re building a system that learns how to work… and that takes iteration.
No code? No problem.
Most tools today are built to help non-developers prototype agents with drag-and-drop logic and natural language.
Start with one agent.
One workflow.
One tool.
That’s more than enough to experience the magic.
🧠 Final Thought: Don’t Just Prompt. Orchestrate.
The magic of agents isn’t in the model… it’s in the loop.
An agent isn’t “smarter” than ChatGPT.
It’s just better structured.
Better scoped.
And built to work toward a goal.
The agentic shift is already underway.
And it’s not just about building bots… it’s about rethinking how work gets done in an AI-first world.
Next on LLMentary:
Now that you have a basic understanding of how LLMs understand you, how to prompt better, using techniques like Fine-Tuning and RAG to supercharge your LLMs’ output, and what AI Agents are… let’s now turn a new leaf and explore everything you can do with an LLM like ChatGPT - we’ll dive into specifics of how one can use chatGPT to the best of your ability and extract maximum use out of it.
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Until then, stay curious.
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