Anthropic ships Computer Use as a production API, not a demo.

Meta has released Llama 3.3 70B with a focus on instruction-following, multilingual coverage, and tool use, positioned as a drop-in replacement for the much larger 405B model. If the cost-per-good-output claims hold on your workload, the economics of self-hosted inference shift again. Worth a benchmark before any commitment to a closed-source tier.

Google has rolled out Gemini 2.0 Flash with native tool use, multimodal output, and an experimental free tier through AI Studio aimed at developers prototyping agent workflows. A genuinely free tier removes one of the last frictions to evaluating Google as a primary model provider. Useful if you have been waiting for a sane prototyping path.

Anthropic has published a pattern catalogue for building agentic systems, distinguishing 'workflows' (LLM + code with predictable control flow) from 'agents' (LLM-driven control flow) and laying out which primitives compose well. Most production agent code today reaches for the most agentic option when a simpler workflow would ship faster and break less. The catalogue gives teams a vocabulary for that decision.

Researchers from Harvard, Stanford, MIT, and Carnegie Mellon have proposed precision-aware scaling laws covering both training and inference, arguing that lower precisions earlier in the pipeline can be cheaper than the field has assumed. If the law generalises, the right precision floor for a given budget moves down a step. Worth tracking if you set inference budgets or train your own models.

DeepSeek has released V3, a 671B-parameter Mixture-of-Experts model, with open weights, a detailed technical report, and reproducible training cost figures. Open weights at this capability tier change the build-versus-buy maths for teams that can host inference. The training cost numbers are also a useful reference point for planning.

LangChain has continued investing in LangGraph as the durable-state layer for multi-agent systems, with new patterns for human-approval gates and persistence across sessions. If you have hit the limits of a single agent loop, the question is which framework's state model survives a real production deployment. LangGraph is one of the few credible answers.

vLLM has continued to ship support for new model architectures, FP8 inference paths, and improved scheduling, keeping pace with the wave of recent open-weight model releases. If you self-host inference, vLLM remains the pragmatic default for high-throughput serving. Worth a re-evaluation if your last benchmark was more than two months ago.

OpenAI has open-sourced an official Python agents SDK with primitives for multi-agent handoffs, guardrails, and tracing, deliberately small enough to read in one sitting. Having a first-party reference implementation reduces the framework-shopping tax for teams that already standardise on OpenAI APIs. Worth comparing against LangGraph and your in-house rig.