WTF are Agents?

Most people overcomplicate Agents.

Are they workflows? are they graphs? are they LLMs in a loop or just expensive while-loops? Are they deterministic, autonomous, or confused? Some say if you whisper "agent" three times, a VC appears with a term sheet.

How about we cut through the noise and understand what an Agent is by mapping out how they work. Let's demystify it — without the hype.

What is an Agent?

Regular programs execute a fixed set of instructions, written as code, in a predetermined order. If you write a program to add two numbers, that's exactly what it will do, every time. It won't add three, or four, or decide to do something else. The outcome is always the same because the logic is hardcoded.

Agents, on the other hand, are AI programs where a language model decides the flow of execution. You give it instructions, a set of tools, and the model decides what to do. If you give an Agent tools to add numbers, it can add two, three, or ten. If you also give it tools to subtract, multiply, and divide, it can perform any combination of operations — without you writing that logic explicitly.

If that explanation sounded abstract, that's because it is. Let's make sense of it by walking through what happens when you run an Agent:

  1. The Agent first builds the context for the model: system messages, user messages, adds chat history, memory, knowledge, state.
  2. It sends that context to the model (the execution loop begins).
  3. The model replies with a message, a tool call, or both.
  4. If a tool is called, the Agent executes it and returns the results to the model. This is what I think makes a program "agentic".
  5. The loop continues until the model produces a final message.
  6. The Agent returns that response to the caller.

That's it, this is an Agent. What'll be different is the context, the tools, and the model's reasoning, but the core remains the same.

We're moving from deterministic execution to reasoning-based execution — from code that follows instructions to software that decides what to do. Will it do it well? We'll find out.

Minimal Example

Let's build a simple agent to demo how it works, we'll add a few capabilities to make it more interesting:

  • A database to store and maintain conversation history
  • Tools via MCP that it can call to answer questions
  • Respond in markdown so it looks pretty

We'll also turn it into a FastAPI app so we can deploy it as a service. You can read the full instructions here.

from agno.agent import Agent
from agno.db.sqlite import SqliteDb
from agno.models.anthropic import Claude
from agno.os import AgentOS
from agno.tools.mcp import MCPTools

# ************* Create Agent *************
agno_agent = Agent(
    name="Agno Agent",
    model=Claude(id="claude-sonnet-4-5"),
    db=SqliteDb(db_file="agno.db"),
    tools=[MCPTools(url="https://docs.agno.com/mcp", transport="streamable-http")],
    add_history_to_context=True,
    markdown=True,
)

# ************* Create AgentOS *************
agent_os = AgentOS(agents=[agno_agent])
app = agent_os.get_app()

You can run this Agent using fastapi dev agno_agent.py and chat with it on the AgentOS UI. Here's how it looks:

Deploy your FastAPI app to your cloud of choice, and you're live!

Are we done?

Not even close. The hard part isn't building the Agent, its building the system that runs these Agents in production, and building a product around it with a great UX (or rather, AX — Agent Experience).

Ensuring reliability, durability, and a smooth experience across thousands of concurrent sessions is where the real engineering happens. These are long-running processes that demand isolated state management, persistent storage, and strong fault tolerance.

Here's what you'll need to consider when building Agents:

  1. Runtime architecture: how agents are orchestrated, manage state, and handle execution loops.
  2. Memory systems: how agents retain and manage context, session history, memory, knowledge and culture.
  3. Tooling integration: how agents connect to APIs, databases, or internal functions (MCPs are popular here).
  4. Safety & Security: how to ensure data, application and user-level security.
  5. Evaluation & performance: measuring usefulness, latency, cost, and reliability of the agentic system.

Each of these is a discipline of its own, with entire startups (sometimes dozens) dedicated to solving. But stitching it all together into a single, cohesive system is still a massive pain.

That's where Agno comes in.

What is Agno?

Agno is a multi-agent framework, runtime, and control plane. It solves the 5 problems mentioned above via 3 tightly coupled components:

  1. Framework for building Agents, Multi-Agent Teams and Workflows. It comes with an incredibly rich set of features like persistent storage, memory management, knowledge retrieval, 100+ toolkits, guardrails, dependency injection, dynamic context management, human in the loop, and much, much more.
  2. Pre-built FastAPI Runtime for deploying multi-agent systems. This runtime, called AgentOS, exposes pre-built endpoints you can build your product on top of. It handles concurrency, state management, and error recovery out of the box — plus extras like initializing MCP connections via lifecycle hooks and securing every request with a security-key.
  3. Control Plane for testing, monitoring, debugging and evaluating multi-agent systems. This is a web interface that allows you to manage your multi-agent systems in real-time. It's a powerful tool that helps you understand what your agents are doing, and why.

If you're building Agents, give Agno a try:

Agents aren't magic. They're just a new kind of software. Once you understand that, everything else falls into place.

Agent Engineering is just Software Engineering