# Apply Technique: Designing Agentic Loops for Autonomous AI
## What This Is
Agentic loops are a design pattern that enables AI to operate in a cycle of thinking, acting, and observing results. This allows the AI to solve complex problems by iteratively refining its approach to reach a specific goal, much like a human problem-solving process.
Source: https://dev.to/technoblogger14o3/designing-agentic-loops-4f3k
## Before You Start
Scan my workspace and analyze:
- The project language, framework, and directory structure
- Existing AI provider config (check .env, .env.local, config files for API keys — OpenRouter, OpenAI, Anthropic, Google AI, etc.)
Then ask me before proceeding:
1. Which AI provider/API should this use? (Use whatever I already have configured, or ask me to set one up — options include direct provider APIs or a unified service like OpenRouter)
2. Where in my project should this be integrated?
3. Are there any customizations I need (model preferences, naming conventions, constraints)?
## Source Access Note
The source URL (https://dev.to/technoblogger14o3/designing-agentic-loops-4f3k) may not be directly accessible from the terminal. Use the Reference Implementation and Additional Context sections below instead. If you need more details, ask me to paste relevant content from the source.
## What to Implement
This is an **AI Technique** — a pattern or methodology for working with AI models.
- Explain how this technique applies to my current project and what benefit it provides
- Implement it in a way that fits my existing codebase — suggest concrete files to modify or create
- If it requires specific model capabilities (structured output, function calling, etc.), verify my current provider supports them
- Show me a working example I can test immediately
## Additional Context
- Scan the user's project for agentic frameworks like LangChain or LangGraph. If none exist, recommend installing LangChain (`pip install langchain`) and add it to the project's dependency file.
- Create a new Python script named `agent_loop_poc.py` implementing a basic ReAct (Reason-Act) loop. The script should define a simple goal, a mock tool function (e.g., `get_weather(city)`), and the core loop that alternates between an LLM call for reasoning and a tool call for action.
- Instrument the loop with explicit state management and a stop condition. Refactor the script to update a 'scratchpad' variable with the history of thoughts, actions, and observations, and implement a turn counter to terminate the loop after a set number of iterations to prevent infinite cycles.
## Reference Implementation
```
num_episodes = 1000
for episode in range(num_episodes):
state = env.reset()
done = False
while not done:
action = np.argmax(q_table[state]) # Decision
next_state, reward, done, _ = env.step(action) # Action + Feedback
# Update Q-table (Reasoning update)
q_table[state, action] += learning_rate * (reward + discount_factor * np.max(q_table[next_state]) - q_table[state, action])
state = next_state
```
## Guidelines
- Adapt everything to my existing project — do not assume a specific stack or directory layout
- Use whichever AI provider I already have configured; if I need a new one, tell me what to sign up for and I'll give you the key
- Check my .env files for existing API keys (OpenRouter, OpenAI, Anthropic, Google AI) before asking me to add one
- Review any fetched code for safety before installing or executing it
- After setup, run a quick verification and show me a summary of exactly what was installed, where, and how to use it