Themesrising
Agents become long-running, parallel and stateful
Agents are moving from single chats to long jobs: parallel agents in ChatGPT Pro, Gemini agents playing Minecraft for 27 hours, durable pi agents and dedicated memory models. Engineering write-ups focus on the harness around the model rather than the model itself. The hard problems are now state, memory and orchestration.
- OpenAINewsAgentsChatGPT Pro can now run multiple agents on one task in parallelGPT-6 Pro splits a task across agents you can monitor and review individually, a sign that parallel multi-agent workflows are moving from dev tools into the mainstream chat product.
- Tencent HyNewsAgentsTencent open-sources Hy Memory 2.0, a model just for agent memoryA 30B (3B active) model, framework and cross-tool sync for deciding what agents should remember. Memory is becoming its own layer of the agent stack rather than a prompt trick.
- EarendilBlogAgentsPi Durable on CloudflareA worked build of long-running pi agents: one session per Durable Object with SQLite storage, hibernation when idle, alarm wakeups and a search subagent.
- Armin RonacherBlogAgentsWhat is CodemodeArmin Ronacher on when having an agent write code beats tool calls, and why bash alone may not be enough. Sharp thinking on agent tool design.
- OpenAI DevelopersReleaseAgentsOpenAI opens the Decisions API to all developersLets apps pick a model, tool or action in near real time from one call, moving routing logic out of agent code and into the platform.