AI-assisted coding: tools, tips, and best practices
Love it or hate it, we’ve entered the era of vibe coding. Instead of asking engineers to turn ideas into code, engineers can instead ask AI to do it for them. A true breakthrough? It looks like it, with OpenAI, Anthropic, and other tech leaders innovating their own AI-assisted coding solutions.
Vibe coding. AI-assisted coding. Whatever you call it, one thing is clear: it’s an evolution in software engineering with real traction and major upside. Our work here is to help technology leaders understand what solutions are out there and what they can do.
How software engineers use AI-assisted coding today
The findings in the latest Anthropic Economic Index are based on the analysis of 500,000 Claude interactions. Three patterns stand out:
For 79% of Claude Code interactions, AI was directly performing tasks;
Coders are using AI to build customer-facing applications;
33% of analyzed Claude Code interactions were start-up based; 13% for enterprise.
Anthropic researchers found that the top use cases for AI-assisted coding were Software Architecture & Code Design and UI/UX Component Development. After that, Debug & Performance Optimization, Web & Mobile App Development, and Technical Documentation Creation rounded out the top five.
The versatility of AI-assisted coding stems from its ability to handle both high-level conceptual work and granular implementation details. This makes it valuable across the development lifecycle, both in terms of productivity and overall efficiency.
Additional use cases include:
Database schema design and query optimization;
API integration and third-party service implementation;
Test suite creation and automated testing strategies;
Code refactoring and legacy system modernization;
Security vulnerability assessment and remediation.
The top AI-assisted coding tools to use
The Anthropic study focuses on Anthropic’s own AI tools. No surprise there. But Claude is far from the only viable tool out there.
Choosing the right tool will depend on your team’s specific needs, expertise, and development environment. The best tool for rapid prototyping, for example, may not be the best tool for multi-file edits; the same goes for debugging, or complex projects with significant business logic.
Multi-model support (GPT-4o, Claude 3.5 Sonnet, Gemini).
Agentic maturity: Capable of varying degrees of autonomy, depending on feature set/mode in use (e.g., co-pilot, agent mode, coding agent). Human-in-the-loop design and multi-modal capabilities.
Company: Cline Bot Inc. Launch: June 2024 Pricing: Free VS Code extension with no subscription fees; pay-per-use via API tokens from your chosen provider; new users start with free credits through Cline account Key features:
Autonomous coding agents with human-in-the-loop approval;
Model Context Protocol (MCP) integration;
Dual Plan/Act modes with workspace snapshots.
Agentic maturity: Sophisticated autonomous capabilities operating within a human-supervised framework.
Agentic maturity: Can autonomously handle full-stack tasks: describe the project, and it builds, tests, and deploys it, all while integrating with the development lifecycle.
Company: Anthropic Launch: October 2024 (research preview launched alongside Claude 3.7 Sonnet); January 2025 (general availability) Pricing: Included with Claude Pro ($20/month) and Max ($100-200/month) Key features:
Terminal-native agentic coding enabling deep codebase understanding and multi-file edits;
Autonomous task execution (codebase search, file edits, testing, committing and pushing code, using command line tools);
Connects with GitHub, GitLab, VS Code, JetBrains, and command line tools;
Supports MCP servers.
Agentic maturity: Sophisticated autonomous capabilities (reflection, planning, tool use, memory); can operate with minimal human oversight.
Company: Qodo (formerly CodiumAI) Launch: 2023 Pricing: Free tier available along with Pro plans for teams/enterprise Key features:
AI test generation;
Code review assistance;
IDE integration;
Focus on code quality and testing.
Agentic maturity: Focused on autonomous test generation with limited scope for broader development tasks.
Best practices for the future of AI-assisted coding
All of these tools require human oversight, especially when they’re touching core business logic. The inherent risks of AI-assisted coding are backed by recent, real-world examples of AI and agentic coding gone wrong.
May contain logical errors, syntax issues, fabricated API, and functions;
Often includes unnecessary “extra” code;
May suggest deprecated approaches or libraries;
Can generate code with security vulnerabilities;
Are dependent on the quality of the prompts;
Can lead to over-reliance and skill atrophy.
Still, for many software teams, the question isn’t will they deploy AI-assisted coding, but when and how.
Having worked with a number of teams using these tools, we find it best to create clear organizational AI policies. These might include:
Attribution standards;
Dependency tracking;
Ethical boundaries;
Code review protocols;
Knowledge transfer requirements;
Compliance verification.
Many teams also find that they can further reduce risk by committing AI-generated code in small, reviewable chunks.
Every day, established players and new innovators keep the AI-assisted coding trend in full swing. AI-assisted software development tools are here to stay. If you’re preparing your organization for an AI-powered future, Transcenda can help. Our team is actively exploring new use cases for AI-assisted coding.
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