Agentic AI Design Patterns

1️⃣ Reflection Loop Pattern - The Secret Behind Self-Improving AI

🧠 Concept
The Reflection Loop Pattern allows an AI agent to review its own output, identify flaws or gaps, and iteratively improve its results. It is much like a human performing code reviews on their own work.

It uses feedback from its own reflection before finalizing results, turning a “chatbot” into a self-critic — better logic, fewer errors.

🧩 Developer’s Analogy
Think of it as “unit testing for reasoning.”
The model doesn’t just produce an answer; it runs an internal feedback cycle to test, debug, and optimize its reasoning before presenting the final result.

⚙ How It Works
1️⃣ Generate an initial output
2️⃣ Critically review it (via reflection prompt or critique model)
3️⃣ Apply improvements or corrections
4️⃣ Repeat until quality stabilizes or performance goals are met

💼 Why It Matters

• Produces higher-quality, more consistent answers
• Reduces hallucinations and reasoning errors
• Enables autonomous improvement without retraining

💡 When:
Output quality significantly outweighs speed considerations and errors carry meaningful consequences. Ideal for:

• Code generation requiring security audits or compliance checks
• Content creation needing factual verification before publication
• Financial analysis where incorrect conclusions risk capital

⛔Limitations:

• Each reflection cycle increases token consumption and latency
• Without well-defined exit conditions, agents can loop unnecessarily
• Critique criteria must be specific and measurable; vague instructions like “check if this is good” produce inconsistent results

💡 Developer Template
You are a senior AI assistant.
Task: [complex task]
Step 1 – Draft: Generate your first attempt
Step 2 – Reflect: Identify weaknesses or gaps
Step 3 – Improve: Rewrite based on your reflection

💻 Example (Software Engineering)
Task: Optimize a Python function for performance.
1️⃣ The model writes initial code.
2️⃣ It reflects: “Loop inefficient — replace with list comprehension.”
3️⃣ It rewrites an optimized version.

By adding a Reflection Loop, the AI no longer acts like a one-shot responder; it behaves like a self-reviewing thinker. Each loop helps the AI:

• Detect gaps in reasoning
• Spot inconsistencies
• Refine before finalizing

This is how agentic systems evolve — gaining depth, reliability, and continuous improvement.

2️⃣ ReAct (Reason + Act) Pattern

🧠 Concept
The ReAct pattern combines reasoning (thinking) and acting (doing) in a single loop.
Instead of generating a full plan first, the agent thinks step by step, performs an action (like calling a tool or retrieving data), then reasons again based on the result.

💡 It’s dynamic decision-making — the agent learns as it acts, just like a human debugging code line-by-line.

🧩Developer Template
You are an intelligent agent that reasons and acts.
For each step:

1. Think: Analyze the current situation or question.
2. Act: Choose an action (e.g., use a tool, search, compute, or output an answer).
3. Observe: Reflect on the result of that action.
4. Repeat until the goal is reached.

💻 Example (Software Engineering)

Task: Find and fix a bug in a backend API.
The agent reasons: “The 500 error might come from database connectivity.”
→ Act: Runs a query test.
→ Observe: Query fails due to null pointer.
→ Reason: Likely uninitialized variable.
→ Act: Suggests fix in the code.

So it reasons, acts, and learns in a feedback loop — just like a developer using a debugger.

3️⃣ Tool-Use Agent Pattern

🧠 Concept
This pattern equips the AI with the ability to use external tools — APIs, databases, web search, code interpreters, or internal functions — when it realizes reasoning alone isn’t enough.

It’s how we move from “text-only intelligence” → to “actionable intelligence.”
The agent decides when and how to call a tool, then reasons based on the result.

You can think of it like a developer using libraries instead of reinventing logic each time.

💡 Developer Template
You are an AI assistant that can use tools to complete tasks.
Available tools:

• [Tool1]: [What it does]
• [Tool2]: [What it does]

For each task:

1. Reason about the goal.
2. Decide if a tool is needed.
3. If yes, generate the proper input for the tool.
4. Observe tool output.
5. Integrate results into your final reasoning.

💻 Example (Software Engineering)

Scenario: An agent debugging a microservice issue.
It has access to:

kubectl for cluster status

curl for API testing

log_reader() for error logs

The agent reasons → calls kubectl get pods → observes crashloop → checks logs → suggests fix.
That’s tool-use reasoning — chaining logic with real system actions.