A comprehensive guide to understanding and implementing autonomous AI agents
Welcome to this multi-part series on building agentic AI systems in Rust. If you’ve been working with traditional RAG (Retrieval-Augmented Generation) pipelines and wondered how to give your AI more autonomy—the ability to reason, plan, and take actions—this series is for you.
We’ll explore a production-ready architecture called Profile + Stepper + Executor that cleanly separates concerns and enables building everything from simple extractors to complex multi-tool agents.
What is an Agentic Agent?
An agentic agent is an autonomous system that can reason, plan, and take actions to accomplish goals. Unlike traditional request-response systems, agentic agents exhibit four key behaviors:
- Reason about problems before acting
- Decide which tools to use and when
- Iterate through multiple steps until the goal is achieved
- Adapt based on intermediate results
Think of the difference between a calculator and a research assistant. A calculator takes input and produces output in a single pass. A research assistant might search multiple sources, evaluate what they find, refine their search, and synthesize information across iterations before delivering an answer.
Traditional RAG vs Agentic RAG
The fundamental distinction lies in how these systems process queries.
Traditional RAG follows a linear, single-pass flow:
Query → Retrieve → Generate → AnswerThe system receives a query, retrieves relevant documents, generates a response based on those documents, and returns an answer. It’s predictable and efficient, but limited.
Agentic RAG introduces a reasoning loop:
Query → Think → Act → Observe → [loop until done] → AnswerThe agent receives a query, reasons about what it needs to do, takes an action (which might be using a tool, searching, or generating), observes the result, and then decides whether to continue reasoning or deliver a final answer.
Key Characteristics
| Aspect | Traditional RAG | Agentic RAG |
|---|---|---|
| Reasoning | None | Multi-step |
| Tool Use | Fixed pipeline | Dynamic selection |
| Iterations | Single pass | Multiple loops |
| Adaptability | Static | Self-correcting |
| Complexity | Simple queries | Complex tasks |
When to Use Each Approach
Understanding when to use agentic systems versus traditional RAG is crucial for building efficient applications.
Use Agentic Agents When:
- Tasks require multi-step reasoning: “Find all products mentioned in our last quarter’s reviews and compare their sentiment scores”
- Multiple tools may be needed: The agent might need to search, calculate, and format data
- The path to solution isn’t predetermined: Open-ended research questions
- Self-correction is valuable: When initial results might be incomplete or incorrect
Use Traditional RAG When:
- Simple question-answering: “What is our return policy?”
- Single retrieval is sufficient: Direct lookups with clear answers
- Latency is critical: Real-time applications where speed matters
- Predictable behavior is required: Compliance or audit scenarios
What’s Coming Next
In this series, we’ll cover:
- Part 2: Core Architecture - The Profile + Stepper + Executor design pattern and why it matters
- Part 3: Context Management - Understanding AgentContext vs ExecutionContext
- Part 4: The Stepper Pattern - Implementing different reasoning strategies (ReAct, Function Calling)
- Part 5: Middleware Pipeline - Adding cross-cutting concerns without modifying core logic
- Part 6: Building Tools - Creating type-safe, composable tools for your agents
- Part 7: Putting It All Together - Complete implementation examples
Each part will include working Rust code, architectural diagrams, and practical examples you can adapt for your own systems.
Next up: Part 2 - Core Architecture: Profile + Stepper + Executor
This series is based on the Reflexify agentic architecture, designed for production multi-tenant SaaS applications.