Why giving AI agents more compute isn't enough—and what Meta proposes instead

VentureBeat · Ben Dickson · 2026-10-09 04:00 UTC

Giving agents more compute doesn't always improve results. Meta's new framework helps them decide how to spend their budgets on productive work.

AI agents often struggle to assess their own progress and decide what to do next. They can waste compute resources following failed approaches, or they might overlook promising results or stop before completing a task. These problems become more costly as agents take on longer and more complex workflows.

Researchers at Meta Superintelligence Labs have developed a framework called agentic meta-reasoning that addresses this challenge by enabling the agent to have a dedicated reasoning stream on what to do next. The framework enables agents to evaluate their progress, explore alternative approaches, and direct resources toward the most promising next steps.

On ProgramBench, a software reconstruction benchmark, the framework achieved a 71.5% average hidden-test pass rate with GPT-5.5, compared with 58.0% for Codex in the researchers' evaluation.

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