Beyond Copilot: Truly Agentic Coding

Code completion tools are great, but what if your assistant could truly understand your codebase, plan refactoring steps, and execute changes across multiple files? That’s the promise of agentic coding, and Interacly provides the building blocks.

Why Agents Excel at Coding Tasks

• Contextual Understanding: Agents can ingest large codebases (via RAG on vector stores) to grasp context beyond the current file.
• Planning & Decomposition: Complex tasks like “migrate this component to the new design system” can be broken down into smaller, executable steps.
• Tool Use: Agents can invoke linters, formatters, test runners, and even version control systems via integrated tools.
• Multi-step Execution: Unlike simple completion, agents can perform sequences of actions: find relevant files, analyze code, propose changes, apply them, and run tests.

Building Blocks in Interacly

Creating a coding assistant involves composing several key components:

1. Code Analysis Tools: Integrate tools that use tree-sitter or Language Server Protocol (LSP) capabilities to parse and understand code structure.
2. File System Tools: Allow the agent to read, write, and list files within the project directory (with appropriate safety sandboxing).
3. Vector Store (RAG): Embed the project’s codebase into a vector store for semantic search and context retrieval.
4. Planning Module: Use a powerful LLM with a specific prompt instructing it to break down coding requests into actionable steps using the available tools.
5. Execution Agent: An agent focused on reliably executing the planned steps, handling errors, and potentially asking for clarification.

Example Workflow: Automated Refactoring

1. User Request: “Refactor all instances of OldButton component to use the new NewButton component, mapping the variant prop to the intent prop.”
2. Planning Agent:
   • Use file system tools to find all files importing OldButton.
   • Use code analysis tools to identify specific usage locations and prop values.
   • Generate a plan: For each file, replace import, replace component tag, map props.
3. Execution Agent:
   • Iterate through the plan, applying changes using file system tools.
   • (Optional) Use a formatting tool to clean up modified files.
   • (Optional) Use a testing tool to run relevant unit/integration tests.
4. Report: Inform the user of successful changes or any errors encountered.

The Interacly Advantage

Interacly’s visual orchestration canvas makes defining these multi-agent workflows intuitive. You can chain planning and execution agents, configure tool access, and monitor the process in real-time.

While building a fully robust coding assistant is complex, Interacly provides the essential infrastructure to start experimenting with agentic development workflows today. Try building a simple file-reading agent as your first step!