Master Agentic Coding with Gemini CLI Tips and Tricks

For developers and engineers exploring the frontiers of AI-assisted programming, the concept of agentic coding is a game-changer. It moves beyond simple code completion to a collaborative workflow where an AI agent can reason, plan, and execute complex development tasks. While web interfaces are useful, the true power for technical users is unlocked through the command line. Google's Gemini CLI is a potent tool for building these workflows, and with a few key techniques, you can transform it from a simple chatbot into a powerful coding partner.

Beyond Basic Prompts: Structuring Multi-Step Agentic Workflows

The core of agentic coding is breaking down a large, ambiguous task into a series of manageable, reasoned steps. The Gemini CLI excels here when you guide it with strategic prompts. Instead of asking “write me a login function,” you can orchestrate a more sophisticated interaction.

Example: The Multi-Step Refactor
Imagine you need to refactor a function. A basic prompt might give you a single, potentially flawed, output. An agentic approach looks like this:

# Step 1: Analysis
cat messy_function.py | gemini -p "Analyze this code. Identify potential bugs, inefficiencies, and style violations."

# Step 2: Planning
cat messy_function.py | gemini -p "Based on the previous analysis, create a step-by-step plan to refactor this code for clarity and performance."

# Step 3: Execution
gemini -p "Here is the original code and a refactoring plan. Output ONLY the final, refactored code in a code block. Original: $(cat messy_function.py) Plan: $(cat plan.txt)"

By separating analysis, planning, and execution, you force the model to reason explicitly, leading to higher-quality, more reliable results that you can audit at each stage.

Power User Tricks for the Gemini CLI

To fully integrate the Gemini CLI into your development shell, leverage these advanced features that enable true agentic behavior.

1. Leverage System Instructions for Consistent Behavior

System instructions are a powerful way to set the AI's persona and behavior for an entire session. You can pre-define your coding agent's characteristics, ensuring consistent output format and reasoning style. Save your instructions in a file (agent_instructions.txt) and reference it.

gemini --system-instruction "$(cat agent_instructions.txt)" -p "Now, let's build a Python API client."

Your instructions file could contain: “You are an expert software engineer. Always think step-by-step. Prefer readable code over clever one-liners. Output code in a markdown code block.”

2. Generate and Process Structured Data

AI Agents often need to work with structured data like JSON or YAML. You can instruct the Gemini CLI to output in these formats, which can then be piped directly into other command-line tools like jq.

# Generate a project structure as JSON
gemini -p "List the files and folders for a new Python data analysis project. Output as a valid JSON array." | jq .

# Create a configuration file
gemini -p "Generate a basic docker-compose.yml for a Redis and Postgres setup." > docker-compose.yml

This turns the AI into a dynamic data generator that fits seamlessly into automated scripts and infrastructure-as-code workflows.

3. Interactive Shell and File I/O for Complex Tasks

For the most complex agentic tasks, use the interactive shell (gemini -i) to maintain context across a long, multi-turn conversation. Combine this with reading and writing files to create a persistent workspace for your AI agent.

# Start an interactive session with context from your project
gemini -i -p "I'm building a Flask app. Here is my current app.py: $(cat app.py)"

# Then, in the interactive session, you can give sequential commands:
# > Now, add a new /api/data endpoint that returns a list of items in JSON.
# > Great. Now, write a unit test for that endpoint to a new file called test_api.py.

This approach mimics a true collaborative pairing session, allowing you to guide the AI through a complex coding task from start to finish.

Key Takeaways for Your Agentic Workflow

• Think in Steps: Decompose large tasks into analysis, planning, and execution phases for more robust outcomes.
• Use System Instructions: Define your AI agent's role and output preferences for consistency.
• Embrace Structured Data: Output JSON and YAML to pipe AI-generated content into other tools.
• Leverage the Interactive Shell: Maintain context for complex, multi-turn development tasks.

The Gemini CLI is more than just a command-line interface to a large language model. When used with these agentic coding principles, it becomes a powerful engine for automating and augmenting your software development process. By moving from single-query interactions to structured, multi-step workflows, you can harness the full potential of AI as a reasoning partner, ultimately writing better code, faster.