🧬 Anatomy of an Agent

Most teams start with a single prompt and a model call. But to move from a prototype to something dependable, you need to treat behavior like a system.

An agent is a unit of composable intelligence — built from modular components that let you design, evaluate, and evolve behavior safely.

🧩 Core Components

Every production-grade agent includes:

1. Inputs

Agents work on structured inputs — whether from an API call, a webhook, or another agent.

Inputs should be typed, validated, and versioned. This lets you:

• Avoid silent failures
• Enforce consistency across steps
• Make agents interoperable with others

2. Tools

LLMs aren’t databases. Good agents offload specific tasks to tools — like:

• Document retrieval
• Querying an API
• Fetching metadata or lookups

Tools are defined in code, but invoked from prompts — keeping the logic clean and explainable.

3. Prompts (as Config)

Prompts are not embedded in your codebase. They’re stored as config:

• Versioned and testable
• Easy to compare and evaluate
• Safe to update without redeploying code

Prompt config includes:

• Template with variables
• Output schema or expectations
• Metadata (e.g. which task or use case it supports)

This makes prompts a first-class surface — observable, debuggable, and evolvable.

4. Reasoning Flow

Agents often need more than one step:

• Understand intent
• Fetch data
• Apply logic
• Generate output

This flow should be explicit, not implicit. Autogen agents manage this via planning and multi-step chaining. With Aegis, we layer observability and evaluation on top.

5. Memory and Context

Some agents need to remember past steps, results, or user actions.

Memory can be:

• Short-term: relevant to this task only
• Long-term: user or session-specific context

Designing for memory means being deliberate about what’s stored, surfaced, and reused.

🏗️ Choosing an Agent Framework

You can build agents from scratch, stitch together open-source frameworks like LangChain, or adopt a structured, opinionated framework like Autogen.

At Aegis, we build on Autogen as the base — but with production-oriented layering for:

• Prompt and agent config
• Evaluation
• Observability
• Workflow orchestration

This gives you the flexibility of open source with the reliability of a platform.

📄 Example: AutoMarking Agent (Autogen Config)

Here’s a simplified example of an Autogen AssistantAgent defined via config for evaluating free-text answers:

{
  "name": "auto_marker",
  "llm_config": {
    "model": "gpt-4",
    "temperature": 0,
    "system_message": "You are a strict but fair academic marker. Grade the student's answer against the marking rubric."
  },
  "tools": ["retrieve_rubric", "fetch_sample_answers"],
  "input_schema": {
    "question": "string",
    "student_answer": "string",
    "rubric": "string"
  },
  "output_schema": {
    "score": "number",
    "feedback": "string",
    "confidence": "number"
  }
}

This config lives outside of your Python codebase — which means:

• You can A/B test versions
• Product can tweak the tone or logic without asking engineering
• Evaluation tools can trace performance back to specific config versions

This is what “prompting as infrastructure” looks like.

✅ What This Enables

• Testability: Inputs, outputs, and behavior can be evaluated
• Reusability: Agents can be composed into graphs or workflows
• Safety: Prompts and tools can be changed independently

By breaking agents into these parts, you make them easier to scale, evolve, and trust — the same way modern software is built.

Next: How to measure if your agents are actually working.