AI
AI agents and agentic workflows
How AI agents differ from chat assistants, the current frameworks, what they're actually good at, and the failure modes.
AI agents extend the LLM pattern beyond single-turn answering: the model plans, takes actions through tools, observes the result, and iterates until a task is complete. The technical pieces — tool calling, planning, memory — are now standardised enough that most large engineering organisations are running internal agent pilots.
Notifire's coverage of this area is focused on what actually ships to production versus what's a demo. Agent reliability under real-world conditions is the open problem; the frameworks competing to solve it shift monthly.
Latest briefings on AI agents and agentic workflows
AI
Google Reportedly Developing AI Agent Remy
Google is reportedly developing a new AI agent named Remy, designed to perform actions on a user's behalf. According to unconfirmed reports, Remy is being tested internally with Gemini and can integrate with other Google services. The company has not officially commented on the project's existence.
Neeraj Dhiman ·
AI
Fixing Code Bugs With AI Agents
GitLab explains how AI coding agents like Codex can accelerate bug fixing. These tools operate within the terminal to read code, suggest solutions, and run commands. While AI speeds up the initial coding, the full development lifecycle—including reviews and CI/CD pipelines—still requires human oversight.