What is Agentic Software Development?
The spectrum from autocomplete to autonomous agents
AI-assisted coding has evolved through distinct generations, each fundamentally changing how developers interact with their tools.
The evolution of AI coding tools
Generation 1: Autocomplete (2018) Tools like TabNine offered context-aware autocomplete, predicting the next few characters or lines. The developer typed, the tool suggested, the developer accepted or ignored.
Generation 2: Multi-line Suggestions (2021) GitHub Copilot expanded to multi-line suggestions powered by LLMs. Entire functions from comments, but developers still manually evaluated and integrated.
Generation 3: Chat Assistance (2023) Conversational AI embedded in IDEs. Describe problems in natural language, receive explanations or code blocks. Still required copy-paste integration.
Generation 4: Agentic Tools (2025) Autonomous agents that read codebases, write files, execute commands, run tests, and iterate on failures all without manual integration.
The agentic difference
Agentic tools mark a fundamental break from previous patterns. Rather than suggesting code for developers to integrate, agentic tools can:
- Read entire codebases
- Write files directly
- Execute commands
- Run tests
- Iterate on failures
All autonomously. The developer's role shifts from writing code to directing an agent that writes, executes, and validates code on their behalf.
Why this distinction matters
Using an autocomplete tool effectively requires typing speed and pattern recognition.
Using an agentic tool effectively requires a different skill set entirely:
- Clear problem decomposition Breaking work into chunks an agent can execute
- Effective context curation Providing the right information at the right time
- Intervention judgment Knowing when to let the agent continue versus when to redirect