All tags
Topic: "agent-systems"
not much happened today
gpt-5.3-codex claude-opus-4.6 nanochat-gpt-2 openai anthropic langchain agent-systems ai-engineering benchmarking software-organization sandboxing tracing state-management recursive-language-models context-management karpathy sama swyx omarsar0 hamelhusain deepfates
AI News for early February 2026 highlights a detailed comparison between GPT-5.3-Codex and Claude Opus 4.6, with users noting Codex's strength in detailed scoped tasks and Opus's ergonomic advantage for exploratory work. Benchmarks on Karpathy's nanochat GPT-2 speedrun show Opus 4.6 achieving better wall-clock performance, while Codex-5.3-xhigh sometimes suffers from context issues. Karpathy cautions that current models are not yet reliable for fully autonomous AI engineering. Discussions on agent swarms reveal emerging parallels to software organizational design, with Anthropic-style agent coordination systems and LangChain/LangSmith emphasizing environment engineering through tracing, sandboxing, and state control. The concept of Recursive Language Models (RLM) is introduced as a future direction for agent systems to reduce context rot and improve structured communication.
not much happened today.
gpt-5.2-codex glm-4.7 openai cursor github cerebras modal artificial-analysis vllm long-running-tasks autonomous-agents code-generation inference-speed latency batch-inference gpu-scaling model-evaluation agent-systems operational-scaling swyx kevinweil pierceboggan mntruell scaling01
OpenAI launched GPT-5.2-Codex API, touted as their strongest coding model for long-running tasks and cybersecurity. Cursor integrated GPT-5.2-Codex to autonomously run a browser for a week, producing over 3 million lines of Rust code. GitHub incorporated it into their code tools, easing enterprise adoption. Discussions highlight the importance of review loops in agent systems and debate evaluation metrics for coding models. OpenAI partnered with Cerebras to improve inference speed and latency, with Cerebras serving GLM-4.7 at 1,445 tokens/sec and low latency. Provider benchmarking reveals tradeoffs in throughput, latency, and context window sizes. Modal shared operational scaling insights for self-hosted inference fleets of 20k GPUs, focusing on batch inference optimization with vLLM and FlashInfer backend. This reflects a focus on inference infrastructure, long-horizon autonomous agents, and coding model evaluation.
not much happened today
claude-opus-4.5 qwen-3-4b qwen-3-8b qwen-3-14b deepseek-r1 anthropic booking.com perplexity-ai langchain claude scaling01 deepseek qwen prefect agent-systems multi-agent-systems reasoning benchmarking cost-efficiency model-optimization long-context memory-management reinforcement-learning model-performance multi-agent-communication latent-representation inference-cost software-integration jeremyphoward alexalbert__ omarsar0 lingyang_pu dair_ai
Anthropic introduces durable agents and MCP tasks for long-running workflows, with practical engineering patterns and integrations like Prefect. Booking.com deploys a large-scale agent system improving customer satisfaction using LangGraph, Kubernetes, GPT-4 Mini, and Weaviate. Perplexity rolls out user-level memory and virtual try-on features. Claude Opus 4.5 leads on LisanBench and Code Arena WebDev benchmarks with mixed community feedback on its "thinking" and "non-thinking" modes, while improving cost-efficiency and UX with batch APIs and context compaction. Research on multi-agent systems shows LatentMAS reduces communication tokens by 70-84% and improves accuracy using Qwen3 models, and reasoning trace distillation achieves significant token reduction with maintained accuracy, highlighting the importance of reasoning trace style.
not much happened today
kimi-k2 qwen3-next nemotron-nano-2 granite-4.0 gpt-4.5 copilot codex vllm perplexity-ai ibm anthropic graphiti claude cursor-ai microsoft mixture-of-experts model-integration cloud-computing hybrid-models benchmarking agent-systems memory-persistence semantic-search code-retrieval context-length-optimization tool-use evaluation-frameworks software-development scaling01 cedric_chee aravsrinivas omarsar0 _avichawla pierceboggan jo_parkhurst jyangballin ofirpress ml_angelopoulos
Kimi-K2 Reasoner has been integrated into vLLM and will soon be supported by SGLang, featuring a massive 1.2 trillion parameter MoE configuration. Perplexity AI released research on cloud-portable trillion-parameter MoE kernels optimized for AWS EFA, with potential integration into vLLM. IBM's vLLM team formalized hybrid dense and sparse expert models, supporting models like Qwen3-Next, Nemotron Nano 2, and Granite 4.0. Kimi-K2 reportedly scores 77% on GPQA Diamond, outperforming GPT-4.5 at 71.4%, though this is unverified.
Anthropic published a guide on efficient tool-heavy agent systems using MCP patterns, drastically reducing context tokens by ~98.7%. Graphiti MCP demonstrated shared memory across apps like Claude Desktop and Cursor for persistent agent memory. VS Code introduced an "Agent sessions" feature to unify agent management, including Copilot and Codex. Cursor AI improved coding accuracy via semantic search and code retrieval embeddings. New evaluation frameworks like CodeClash and LMArena assess agent and coding model performance in realistic multi-round tasks and occupation-tagged leaderboards.