GPT-5.6 moved the agent loop inside the model, and it's aimed straight at your framework

Everyone covered the ChatGPT Work launch. Almost nobody read the API changelog, which is where the story actually is.

GPT-5.6 shipped two things that change how you build agents. First, Programmatic Tool Calling: instead of the model returning one tool call at a time to your loop, it writes JavaScript that runs in an isolated, network-less V8 runtime and orchestrates many tool calls itself. It's ZDR-compatible. The model chains the calls, keeps intermediate results out of the conversation, and hands you back only the final answer. Second, a Multi-agent beta where one GPT-5.6 instance spawns concurrent subagents and synthesizes their work inside a single request (MarkTechPost / OpenAI Developers).

Read that again. The two hardest parts of building an agent, orchestrating dependent tool calls without blowing up your context and fanning out to subagents, just became a model feature you don't write.

What makes this more than a one-vendor thing: Anthropic shipped the exact same primitive in the same window. Their Programmatic Tool Calling tags orchestratable tools with "allowed_callers": ["code_execution_20250825"] so Claude writes one Python script that chains calls with asyncio.gather() and only the final result re-enters context. Their measured numbers are concrete: 43,588 tokens down to 27,297 (a 37% cut), GAIA jumping 46.5% to 51.2%, and 19-plus inference passes eliminated in a 20-tool workflow (Anthropic Engineering). Two frontier labs, two weeks, one idea.

Here's my honest read. If your agent framework's main job is looping over tool calls and stitching subagent output together, a chunk of it is now commodity. The value isn't in the plumbing anymore. It's in what you feed the loop and how you check what comes out. I've hand-rolled this orchestration layer three times across my projects and it was always the fiddliest, most bug-prone code I owned. Giving it up doesn't sting.

What to do this week: if you've got any workflow with 3+ dependent tool calls or fan-out over a list, port it to programmatic tool calling on whichever lab you're already on. You collapse the round trips and the latency at the same time, and the token savings are real, not marketing. Just don't fall for the multi-agent framing without reading story 4 below on the 15x token premium first.