The stagnation take holds up until you ask it to compete with actual evals. Coherent 30-minute agent runs, tool-call reliability on complex schemas, long-context retrieval that finally works. The progress is there, it just isn't a dunk thread.
AGENTS
-
Agentic RAG: Planning Over Agent Labels in Retrieval Systems
By
–
The real contrast isn't static retrieval vs. adaptive. It's whether the retrieval layer can decide to stop, re-plan, and try a different tool. Agentic RAG is just RAG with a planner in front of it. The name is new, the accuracy gain is from the planning, not the "agent" label.
-
Clarifying the terminology surrounding AI agents
By
–
On remplace pas le mot agent par harness. C'est juste un mot en plus pour expliquer un concept autour des agents.
-
AI Agent Workflows: Impressive Slides, Unclear Execution
By
–
-
The importance of ‘harnessing’ for AI agents
By
–
Si vous devez apprendre un mot ce week-end, c’est le « harness ». En bref, c’est tout ce qui permet à un agent de passer de la compréhension à l’action. Un modèle d’IA peut être très bon pour répondre de manière stricte ou utiliser des outils spécifiques, mais ça ne veut pas
-
Using Grok AI Agent for Self-Reflection and Personal Insight
By
–
Should I have Grok stimulate me again? Why you might be right it can tell you more about how I think than I can.
-
AI Code Efficiency: Agents Optimize Performance Through Refactoring
By
–
Garry reveals a ton about his AI code statements. Who cares about how many lines of code the AI writes? It is what it does that matters. But a good refactor is to ask your agents to do an efficiency review. Mine found ways to get faster.
-
Automated Testing and TDD Best Practices for AI Development
By
–
Step one is to have good automated tests – then approach every p by researching, planning, implementing with red/green TDD, then validating with both human-manual and coding-agent-manual testing
