The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation

• URL: http://arxiv.org/abs/2608.19263v1
• Date: Tue, 18 Aug 2026 07:14:36 GMT
• Title: The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation
• Abstract summary: This paper explores the critical methodologies for evaluating AI agents and the essential role of advanced observability infrastructure.<n>We propose the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a contract layer for agentic systems.<n>We describe an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evolution of artificial intelligence has necessitated a fundamental shift from evaluating isolated Large Language Models (LLMs) to assessing autonomous agentic architectures. This paper explores the critical methodologies for evaluating AI agents and the essential role of advanced observability infrastructure. We analyze the architectural components of agents and identify the severe limitations of current evaluation paradigms, including benchmark exploitation, the "confidently wrong" phenomenon, and the discrepancy between theoretical capability and operational reliability. To begin addressing the fragmentation in current evaluation infrastructure, this paper proposes the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a portable evaluation contract layer for agentic systems. In its current form ECP defines a small JSON-RPC interface over which an agent exposes its user-visible output, the tool calls it made, and evaluator-safe audit context, and against which programmatic checks can be run uniformly across frameworks and continuous integration systems. We describe an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI, and we situate the design against failure modes documented in the recent literature. ECP is presented as work in progress rather than a finished standard: the evaluation surface, method set, and grader families are all expected to change as the protocol is exercised against more systems, and the empirical validation required to justify adoption is outlined as future work.

Related papers

• AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities [55.23913617185508]
  Agent is an open-source, lightweight, and infrastructure for evaluating Large Language Models (LLMs)-based agents.<n>It organizes the evaluation process around three independent components, namely Benchmark, Harness and Environment.<n>It features a fault-tolerant runtime and comprehensive trajectory analysis tools to transparently diagnose failure modes like reward-hacking.
  arXiv  Detail & Related papers  (2026-07-15T11:11:17Z)
• Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems [64.88044474617854]
  The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive systems.<n>This survey introduces a unified taxonomy grounded in the level of autonomy and three core paradigms of agentic recommender systems.
  arXiv  Detail & Related papers  (2026-07-05T17:55:05Z)
• From Holistic Evaluation to Structured Criteria: Rubrics Across the Evolving LLM Landscape [79.30826980815927]
  rubrics are explicit criteria sets that transform complex quality judgments into structured and actionable standards.<n>We systematically organize existing rubric designs, examine their construction and optimization, and analyze their role across evaluation and training.
  arXiv  Detail & Related papers  (2026-06-07T13:34:55Z)
• The Meta-Agent Challenge: Are Current Agents Capable of Autonomous Agent Development? [80.24951682268332]
  We introduce the Meta-Agent Challenge (MAC), an evaluation framework designed to test the capacity of frontier models for autonomous agent development.<n>To ensure evaluation integrity, this framework is secured by multi-layer defenses against reward hacking.<n>We demonstrate that meta-agents rarely match human-engineered baseline policies, and the few that do are dominated by proprietary frontier models.
  arXiv  Detail & Related papers  (2026-06-03T04:58:17Z)
• Interactive Evaluation Requires a Design Science [90.62695599188204]
  Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other agents.<n>The field has begun to build interactive benchmarks, but the resulting landscape is fragmented.<n>This position paper argues that interactive evaluation should be treated as a principled evaluation paradigm.
  arXiv  Detail & Related papers  (2026-05-18T04:03:18Z)
• Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility [9.928824688627037]
  We argue that generative AI evaluation requires a paradigm shift from static benchmark-centered transparency toward stakeholder, goal, and context-conditioned utility transparency grounded in human outcome trajectories.<n>We propose SCU-GenEval, a four-stage evaluation framework consisting of stakeholder-goal mapping, construct-indicator specification, mechanism modeling, and longitudinal utility measurement.
  arXiv  Detail & Related papers  (2026-05-07T18:56:07Z)
• Beyond Benchmark Islands: Toward Representative Trustworthiness Evaluation for Agentic AI [45.21562889170875]
  We propose a systematic evaluation paradigm that characterizes agent trustworthiness over a scenario manifold.<n>The framework integrates four complementary components: static cognitive and policy analysis, (ii) interactive sandbox simulation, (iii) social-ethical alignment assessment, and (iv) a distribution-aware representative sampling engine.
  arXiv  Detail & Related papers  (2026-03-16T08:51:33Z)
• From Prompt-Response to Goal-Directed Systems: The Evolution of Agentic AI Software Architecture [0.0]
  Agentic AI denotes an architectural transition from stateless, prompt-driven generative models toward goal-directed systems.<n>This paper examines this transition by connecting intelligent agent theories, with contemporary LLM-centric approaches.<n>The study identifies a convergence toward standardized agent loops, registries, and auditable control mechanisms.
  arXiv  Detail & Related papers  (2026-02-11T03:34:48Z)
• Beyond Task Completion: An Assessment Framework for Evaluating Agentic AI Systems [0.0]
  Recent advances in agentic AI have shifted the focus from standalone Large Language Models to integrated systems.<n>We propose an end-to-end Agent Assessment Framework with four evaluation pillars encompassing LLMs, Memory, Tools, and Environment.<n>We validate the framework on a representative Autonomous CloudOps use case, where experiments reveal behavioral deviations by conventional metrics.
  arXiv  Detail & Related papers  (2025-12-14T18:17:40Z)
• AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production [4.031479494871582]
  We present Agent, the first evaluation framework designed specifically for post-deployment monitoring and reasoning of agentic pipeline.<n>Agent achieves state-of-the-art results on key metrics, while uncovering critical issues missed in human annotations.
  arXiv  Detail & Related papers  (2025-09-18T05:59:04Z)
• Deep Research Agents: A Systematic Examination And Roadmap [109.53237992384872]
  Deep Research (DR) agents are designed to tackle complex, multi-turn informational research tasks.<n>In this paper, we conduct a detailed analysis of the foundational technologies and architectural components that constitute DR agents.
  arXiv  Detail & Related papers  (2025-06-22T16:52:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.