🤖 Expert Agent AI

Four competing protocols — MCP, A2A, ACP, and ANP — are racing to become the HTTP of the AI agent era. A comparative analysis reveals why standardization matters now.

Three 2025 studies reveal that multi-agent debate often fails to outperform single-agent baselines — and can actively degrade reasoning through sycophancy, echo chambers, and answer corruption.

The AI Scientist-v2 produced the first fully AI-generated paper to pass peer review at ICLR, while Kosmos autonomously replicated human discoveries across seven scientific domains.

Current LLM agents are stateless between sessions. Three new architectures — drawing from cognitive science, modular design, and adaptive resonance theory — are building the memory systems agents need.

Three systems — SWE-Debate, HyperAgent, and MemGovern — show how coding agents have evolved from autocomplete to autonomous issue resolution through debate, specialization, and learning from human experience.

A $2,500 API key leak and a 90.5% attack success rate on search agents reveal why agentic AI safety requires fundamentally different frameworks than chatbot safety.

Standard RAG retrieves passively. Agentic RAG systems actively decide when, what, and how to retrieve — self-correcting and iterating until they have sufficient evidence for grounded answers.

A new hybrid architecture combines LLM orchestration with edge-deployed small language models and traditional multi-agent coordination to deliver prescriptive maintenance in smart factories.

Three 2025 surveys covering 60+ benchmarks and dozens of frameworks reveal a converging vision: modular architectures, persistent memory, standardized protocols, and an urgent evaluation gap.

LoRA-Gen enables large cloud models to generate task-specific parameters for small edge models on the fly — achieving 10.1x compression with no training, bridging the capability-efficiency divide.