Brackett Released Open-Source AI Agent Benchmark
The framework evaluates how AI agents handle operational tasks, business logic, and behavioral retention.
Updated on Sept. 28, 2026 in Artificial Intelligence
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San Francisco-based Brackett has released the Agent Effectiveness Index, an open-source benchmark designed to measure the real-world performance of AI agents. The project, which provides its methodology and scoring code under an MIT license, currently compares three systems including Brackett, OpenAI's Codex, and Anthropic's Claude.
Why it matters
Traditional AI evaluations often prioritize static knowledge, whereas this index addresses the growing need for metrics that confirm whether agents can reliably execute end-to-end business operations. By focusing on process and persistence, the benchmark aims to shift focus from simple task completion to long-term operational utility.
The framework scores systems across three specific areas: business understanding, operational execution, and learning persistence. It evaluates the agent's ability to ground answers in evidence while handling exceptions and retaining taught behaviors.
The players
Brackett
A San Francisco-based developer of a Connected Agentic Workforce platform founded by former technical leaders from Microsoft, Rubrik, and Amazon.
OpenAI
A primary developer of large language models and the creator of the Codex system evaluated in the index.
Anthropic
A research company focused on AI safety and the developer of the Claude series of models included in the benchmark comparison.
The details
The benchmark assesses agents on their capacity for complex process execution, meaning the ability to complete multi-step tasks while managing unexpected errors. It tests behavioral retention—the system's ability to maintain taught skills over time—rather than just testing for single-shot accuracy. The scoring methodology and task set are available as open-source resources, allowing developers to audit how these systems navigate real-world business environments.
Timeline
Brackett launched the Agent Effectiveness Index on September 28, 2026.
The Tech Race
This release directly competes with static knowledge-based benchmarks like the MMLU by shifting focus toward operational reliability and agentic workflows. It positions Brackett to define the metrics for the next phase of agent adoption where persistent, task-capable systems are the primary goal.
Developers and companies can now use the open-source code to audit their own AI agents against standardized criteria for business reliability. The platform is currently in a preliminary phase, with Brackett planning to add further scoring metrics for execution, transfer, and retention in future updates.
The takeaway
The Agent Effectiveness Index highlights a shift toward evaluating AI by its operational durability rather than just its output quality. Interested parties should monitor upcoming updates from Brackett, as the company plans to expand the index to include more rigorous metrics for skill transfer and persistence.
Further reading
For more on how evolving benchmarks are shaping the industry, see our coverage of Artificial Intelligence.
Source note: This article includes information reported by IT Brief US.
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