The year 2025 marks the beginning of the “Agentic Era” in software development, characterized by the emergence of advanced Large Language Models (LLMs) and their applications in coding. Initially, tools like ChatGPT and Claude Code began reformatting coding processes, enabling LLMs to act as “agents” that improve their outputs through a feedback loop. These agents excel in constrained environments, transforming data formats, fixing code styles, and generating compatible methods. The introduction of the Model Context Protocol (MCP) allows these agents to access local resources, enhancing functionality. While trust in agentic systems grows, managing their access is crucial for safety. The rise of parallel workflows, facilitated by tools like Conductor, enables simultaneous task execution in isolated environments. This Agentic Era demonstrates that LLMs are effective in specific contexts, countering skepticism about their utility while emphasizing their potential for refining coding processes in the future.
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