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ARXIV:2603.15483 · AGENTS · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.15483AGENTSSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights.
Opportunity summary
Pain The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable…
Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework.
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels.
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
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The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights.
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Paper Pack
10.48550/arXiv.2603.15483The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights.
Abstract
Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework. Prior works each employ their own methods to determine task success, such as database lookups, regex match, etc., adding complexity to the development of a unified agent evaluation approach. Moreover, they do not systematically account for the user's role nor expertise in the interaction, providing incomplete insights into the agent's performance. We argue that effective agent evaluation goes beyond correctness alone, incorporating conversation quality, efficiency and systematic diagnosis of agent errors. To address this, we introduce the TED framework (Talk, Evaluate, Diagnose). (1) Talk: We leverage reusable, generic expert and non-expert user persona templates for user-agent interaction. (2) Evaluate: We adapt existing datasets by representing subgoals-such as tool signatures, and responses-as natural language grading notes, evaluated automatically with LLM-as-a-judge. We propose new metrics that capture both turn efficiency and intermediate progress of the agent complementing the user-aware setup. (3) Diagnose: We introduce an automated error analysis tool that analyzes the inconsistencies of the judge and agents, uncovering common errors, and providing actionable feedback for agent improvement. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels. We also demonstrate potential gains in agent performance with peaks of 8-10% on our proposed metrics after incorporating the identified error remedies into the agent's design.
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Extraction status
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Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Preparing verified analysis
Dimensions overall score 7.0
PROBLEM
The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework.
METHOD
Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework.
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels.
WHY NOW
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed public claims while anchored extraction refreshes.
The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agent applications are increasingly adopted to automate workflows across diverse tasks. However, due to the heterogeneous domains they operate in, it is challenging to create a scalable evaluation framework.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. We show that our TED framework reveals new insights regarding agent performance across models and user expertise levels.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified April 2026. Public score 7.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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The TED framework enhances agent evaluation by incorporating user roles and automated error analysis for improved performance insights.
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Agents
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Commercial read
7.0/10 public viability
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reason
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proof status
unverified
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next verification path
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Technical feasibility
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Gaps
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Write integration checklist from prototype path and target workflow.
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DEFENSIBILITY
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