Agent Engineering 101: Software, systems, and security in practice

5 hours ago 1

✨ The intersection of software, systems and security engineering.

For a moment, stop debating what an Agent should be — deterministic or autonomous, a workflow or graph. Just pause for a sec and step back.

Our goal is to make use of this technology, which in my opinion, lends itself to 3 major use-cases:

Tools that improve productivity (chatgpt, claude, cursor).
Workflows that saves time (marketing research, report generation).
AI products that solve user problems (eg: Notion AI).

You can buy AI tools, and tools for building workflows, but building AI products is where the real engineering happens. Let's dive in.

What is Agent Engineering?

Agent Engineering is the practice of building, running and managing agentic systems. It sits at the intersection of software engineering, system design and security engineering.

In practice, if you're building an AI product, you'll need an AI backend — a system that connects to your frontend via an API. This backend is responsible for running agents (concurrently), managing memory, knowledge, state, and ensuring the security and privacy of your environment. This is Agent Engineering, which focuses on:

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

Agent Engineers are responsible for answering questions like:

How do we serve our agents as an API that our frontend can call?
When should we use REST versus Websockets?
How do we handle request/response timeouts (29 seconds for aws api gateway)?
If tools are exposed via MCP, how should our AI backend establish and maintain a connection to the MCP server? Should it be initialized once using FastAPI lifecycle hooks, or re-established every time an agent runs (probably not)?
How should authentication and authorization be handled — once (probably not), per request, or through persistent sessions?
How do we manage concurrency and state when multiple users call the same agent? Are sessions properly isolated?
What is the security boundary of each request? Are agents only accessing data permitted by RBAC?
How do we log and monitor the agentic system? Tracing is popular, but it’s not enough. How do we capture events like “this request was made,” “this agent, via this request, accessed this data,” and the complete lifecycle of what happened during execution?

Agent Engineering is not just about building agents, it's about building the system that runs them (securely). Its 40% agent development, 40% system design and 20% security engineering.

How Agno helps with Agent Engineering?

Agno is a multi-agent framework, runtime, and control plane. It delivers a complete solution for building, deploying and managing multi-agent systems via 3 tightly coupled components:

Framework: for building Agents, Multi-Agent Teams and Workflows.
Pre-built FastAPI Runtime: for deploying multi-agent systems.
Control Plane: web interface for managing multi-agent systems.

One frustration I have with most frameworks is that they give you a way to build an agent, but almost no guidance on how to run it in production. Like, how do I serve this as an SSE compatible API that my frontend can call? How do I build a product out of this? This to me, is incomplete, because the real engineering happens after the agent is built. And no, logging (telemetry) and evals is not what makes a system production-grade. Since when did cloudwatch and unit-tests make a product? They're parts of it, sure, but stop selling them as the whole story.

While Agno gives you an incredibly feature-rich agent framework — it's the pre-built FastAPI application that really sets it apart. We call this the AgentOS. This is the real advantage of Agno, the advantage of working with people who've built these types of systems before.

Next, the control plane — our web interface for managing AgentOS — connects directly to your runtime via your browser, letting you test the real performance of your system. This architecture honestly only makes sense once you test it. So give it a try.

It's a novel architecture that makes your setup inherently secure, since your browser connects directly to the runtime, no data is sent to agno, or any external telemetry services or stored outside your cloud, you avoid unnecessary egress and retention costs.

Minimal Example

Okay, let's demonstrate the power of Agno with a simple example. Here's a fully working Agent, with conversation history, access to tools via MCP, deployed as a FastAPI app - in 20 lines of code.

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()

Run your AgentOS using fastapi dev agno_agent.py and chat with it on the AgentOS UI.

Deploy your FastAPI app to your cloud of choice, and voilà, you're live in production. It's impossible to move this quickly without Agno.

Summary: The Layers of Agent Engineering

Agent Engineering has three fundamental layers:

The Framework (Build)

This is where you define your Agents, Teams and Workflows — the schemas, memory, knowledge, and guardrails, the reasoning loop.

The Runtime (Run)

The runtime serves (via API), scales, and orchestrates Agents in production. It handles concurrency, async execution, error recovery, and communication between agents and tools.

The Control Plane (Manage)

The control plane provides visibility: dashboards, monitoring, debugging, and human-in-the-loop control. It's how you understand what your agents are doing — and why.

Agno combines all three. It's not just a framework. It's a complete runtime and control plane for multi-agent systems.

Designed for Agent Engineering

I'll end this article with a list of features of Agno:

CategoryFeatureDescription

Core Intelligence	Model Agnostic	Works with any model provider so you can use your favorite LLMs.
	Type Safe	Enforce structured I/O through input_schema and output_schema for predictable, composable behavior.
	Dynamic Context Engineering	Inject variables, state, and retrieved data on the fly into context. Perfect for dependency-driven agents.
Memory, Knowledge, and Persistence	Persistent Storage	Give your Agents, Teams, and Workflows a database to persist session history, state, and messages.
	User Memory	Built-in memory system that allows Agents to recall user-specific context across sessions.
	Agentic RAG	Connect to 20+ vector stores (called Knowledge in Agno) with hybrid search + reranking out of the box.
	Culture (Collective Memory)	Shared knowledge that compounds across agents and time.
Execution & Control	Human-in-the-Loop	Native support for confirmations, manual overrides, and external tool execution.
	Guardrails	Built-in safeguards for validation, security, and prompt protection.
	Agent Lifecycle Hooks	Pre- and post-hooks to validate or transform inputs and outputs.
	MCP Integration	First-class support for the Model Context Protocol (MCP) to connect Agents with external systems.
	Toolkits	100+ built-in toolkits with thousands of tools, ready for use across data, code, web, and enterprise APIs.
Runtime & Evaluation	Runtime	Pre-built FastAPI based runtime with SSE compatible endpoints, ready for production on day 1.
	Control Plane (UI)	Integrated interface to visualize, monitor, and debug agent activity in real time.
	Natively Multimodal	Agents can process and generate text, images, audio, video, and files.
	Evals	Measure your Agents' Accuracy, Performance, and Reliability.
Security & Privacy	Private by Design	Runs entirely in your cloud. The UI connects directly to your AgentOS from your browser, no data is ever sent externally.
	Data Governance	Your data lives securely in your Agent database, no external data sharing or vendor lock-in.
	Access Control	Role-based access (RBAC) and per-agent permissions to protect sensitive contexts and tools.