Brackett establishes new open source benchmark that scores AI agents on their ability to learn and perform

Brackett is introducing the Agent Effectiveness Index (AEI), released as a free and open-source benchmark which scores and ranks AI agents on their ability to understand complex, real-world processes, take proactive actions, and keep learning without drifting as processes change. In addition to this benchmark, Brackett is also launching its Connected Agentic Workforce platform.

As agents are increasingly tasked with inference and applying context, this includes whether they can make judgment calls, exceptions, and escalation patterns that make a process reliable, or if they're producing inconsistent results at a high cost. It's the equivalent of hiring a company's worth of people with no way to evaluate performance. The Agent Effectiveness Index addresses that gap, according to Brackett.

"Most evaluations for AI agents focus on static knowledge, or what it knows. But this doesn't tell you whether or not that agent can complete the task it was created for, because real world tasks require things like judgement calls, exceptions, or knowing when to bring in a human. These are learned from experiences, not training data. Until now there has been no way to score that capability," said Ehsan Azarnasab, co-founder and chief scientist of Brackett and formerly principal scientist on Microsoft's GenAI platform team.

AEI evaluates agent systems across three dimensions:

• Business Understanding: Whether an agent grasps how a specific company works, and grounds its answers in real evidence rather than plausible guesses.
• Operational Execution: Whether an agent produces correct results, handles exceptions, and stays inside the authority it was given.
• Learning Persistence: Whether teaching an agent genuinely changed its behavior, and whether that holds on new cases, after time passes, and when the rules change.

The full task set, scoring code, and methodology are available on Brackett's GitHub under the MIT License, with scoring for execution, transfer, and retention to follow as the Index expands toward a complete picture of agent effectiveness.

"The next era of work isn't humans versus agents, it's humans and agents becoming genuinely better together," said Jaideep Sarkar, co-founder and CEO of Brackett. "We built Brackett because we've seen a recurring gap in which companies try to automate tasks, but without a way to build compounding, connected intelligence that they actually own. It's also why we're opening the Agent Effectiveness Index to the world. You can't build a trustworthy agentic workforce without a way to measure it."

Brackett's Connected Agentic Workforce Platform is also now live—using its Capture, Codify, and Compound methodology, Brackett turns simple conversations, with no code required, into agents that learn how to execute complex processes.

Agents then progress to running tasks at scale with real consistency and control, and then to acting on the judgment they've learned over time. Brackett connects these agents to each other, to the systems they run in, and to the people who trained them, so every workflow makes the next one smarter across the business. The intelligence the organization builds along the way is retained as something it owns rather than something it rents from a model vendor, the company said.

The Agent Effectiveness Index is available now on Github, free and open source.

For more information www.brackett.ai.