Agentic CLEAR: Framework for Autonomous AI

Updated 5 July 2026

Agentic CLEAR is a multi-level framework defining explicit evaluation of autonomous, tool-using AI systems at node, trace, and system levels.
It employs a dynamic, data-driven taxonomy that discovers domain-adaptive issues, aligning closely with human-annotated errors and success predictions.
The framework is applied across enterprise, academic, and architectural domains to improve cost-efficiency, reliability, and governance of agentic systems.

Agentic CLEAR is an emerging label in agentic-AI literature for frameworks that make autonomous, tool-using, multi-step systems more explicit, evaluable, and governable. In its most direct contemporary usage, it denotes an automatic, dynamic, and easy-to-use evaluation framework for LLM agents that produces textual insights at system, trace, and node granularity (Yehudai et al., 21 May 2026). In adjacent work, CLEAR also appears as a metric framework for enterprise deployment, as a higher-education design principle, and as a shorthand for explicitness, legibility, accountability, and reliability in agentic architectures rather than as a single standardized acronym (Mehta, 18 Nov 2025).

1. Terminological scope and core meanings

The term appears in more than one formulation, and the differences matter because they point to distinct but compatible concerns: evaluation, architecture, governance, and deployment.

Usage	Core meaning	Representative source
Agentic CLEAR	Automatic multi-level evaluation of LLM agents	(Yehudai et al., 21 May 2026)
CLEAR	Cost, Latency, Efficacy, Assurance, Reliability	(Mehta, 18 Nov 2025)
Agentic CLEAR	Coordinated, Learner-centered, Equitable, Agentic, and Resilient use of AI in higher education	(Sudarshan et al., 14 May 2026)

In the evaluation literature, Agentic CLEAR is positioned “above the observability layer,” taking traces from systems such as LangFuse or OpenTelemetry-compatible pipelines and converting them into node-level, trace-level, and system-level critiques and scores (Yehudai et al., 21 May 2026). In enterprise evaluation, CLEAR is a five-dimensional framework designed to move beyond task completion accuracy toward cost-efficiency, latency, policy compliance, and repeated-run reliability (Mehta, 18 Nov 2025). In higher education, “Agentic CLEAR” can be understood as a framework that makes Coordinated, Learner-centered, Equitable, Agentic, and Resilient use of AI, although the underlying paper does not define CLEAR explicitly (Sudarshan et al., 14 May 2026).

A related architectural strand uses CLEAR-like language for explicit, reliable, and auditable agentic layers. DALIA, for example, is described as a declarative, model-independent middle layer that maps well to desiderata such as Clarity, Legibility, Explicitness, Accountability, and Reliability by constraining planning and execution to declared capabilities, tasks, and agent directories (Rodriguez-Sanchez et al., 24 Jan 2026). This suggests that “Agentic CLEAR” is best treated as a family of design commitments rather than a single closed specification.

2. Multi-level evaluation of agent behavior

As a concrete framework, Agentic CLEAR evaluates an agentic system on three levels. The system level aggregates patterns across all tasks and nodes. The trace level evaluates each end-to-end run. The node level evaluates recurring behavior of individual components such as planners, tool-callers, or specialized sub-agents (Yehudai et al., 21 May 2026).

Its formal setup models a dataset of tasks $\mathcal{D} = \{x_n\}_{n=1}^N$ , an agentic system $A$ , and a trace for each task,

$t_n = \{(i_k, o_k, a_k)\}_{k=1}^{K_n},$

where $i_k$ is the input to a step, $o_k$ is the step output, and $a_k$ is the node that produced it. The pipeline applies four judge modes: step-wise evaluation $J_s$ , trace-wise evaluation $J_t$ , rubric generation $J_r$ , and rubric checking $J_v$ . Their outputs are then clustered and summarized by the CLEAR aggregation method into recurring “issues” at node and system scope (Yehudai et al., 21 May 2026).

A central contribution is that the taxonomy is not fixed in advance. Rather than forcing traces into static hand-crafted error categories, Agentic CLEAR discovers domain-adaptive issues directly from the critiques it generates. In experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, it produced “high-quality, data-driven, insightful feedback,” aligned strongly with human-annotated errors and predicted task success rate (Yehudai et al., 21 May 2026).

The empirical comparison to TRAIL-style human annotations is notable. All GPT-5-derived issues and all OSS-120B-derived issues mapped to at least one TRAIL category; GPT-5 issues covered 12 of 12 relevant TRAIL categories, while OSS-120B issues covered 10 of 12. On propagated trace-level category prediction, GPT-5 with full and partial matches reached Micro F1 $A$ 0 and Macro Category F1 $A$ 1, outperforming random and frequency-based baselines (Yehudai et al., 21 May 2026). The same framework also used trace scores, rubric scores, and average step-wise scores to predict binary success by AUC, with trace-level scores often strongest.

A common misconception is that observability alone suffices for agent debugging. Agentic CLEAR rejects that premise: logs, spans, and timings show what happened, but they do not automatically expose why tasks fail, which nodes systematically misbehave, or which error patterns recur across heterogeneous traces (Yehudai et al., 21 May 2026).

3. CLEAR as an enterprise evaluation doctrine

In enterprise settings, CLEAR denotes Cost, Latency, Efficacy, Assurance, and Reliability. The framework was introduced because existing agentic benchmarks were found to neglect cost control, repeated-run consistency, and operational constraints such as security, policy compliance, and SLA adherence (Mehta, 18 Nov 2025).

The enterprise CLEAR framework formalizes several metrics. Cost-normalized accuracy is described as “accuracy per dollar,” scaled by 100. Cost per success captures total spend divided by successful tasks. SLA compliance rate measures the fraction of tasks completed within domain-specific latency thresholds. Policy adherence score measures violations over policy-critical actions. Reliability is measured through pass@k, especially pass@8, to approximate whether an agent can handle a short sequence of similar tasks without failure (Mehta, 18 Nov 2025).

The motivation is empirical rather than merely conceptual. The study reports three benchmark-wide failures of accuracy-only evaluation: absence of cost control leading to 50x cost variations for similar precision, inadequate reliability assessment where performance drops from 60% single-run to 25% at 8-run consistency, and missing multidimensional metrics for security, latency, and policy compliance. Across six leading agents on 300 enterprise tasks, optimizing for accuracy alone yielded systems 4.4–10.8x more expensive than cost-aware alternatives with comparable performance. In an expert study with $A$ 2, the composite CLEAR score correlated with deployment readiness at $A$ 3, compared with $A$ 4 for efficacy-only evaluation (Mehta, 18 Nov 2025).

This usage of CLEAR is narrower than Agentic CLEAR as a general design ideal, but it has become foundational because it makes agent deployment legible in operational terms. It also clarifies a second misconception: a highly accurate agent may still be economically, temporally, or policy-wise unacceptable in production (Mehta, 18 Nov 2025).

4. Architectural explicitness, routing, and formal semantics

A large part of the Agentic CLEAR discourse concerns making agent systems structurally explicit. DALIA is exemplary here. It introduces a capability semantic model, an Agentic Task Discovery Protocol, a federated agent directory, and deterministic task orchestration. The essential move is to separate discovery, planning, and execution so that task graphs are built only from declared capabilities and eligible agents, reducing hallucinated actions, unexecutable plans, and brittle multi-agent coordination (Rodriguez-Sanchez et al., 24 Jan 2026).

A complementary formalization treats agentic systems as a host agent model plus a task lifecycle model. The host agent interacts with the user, resolves intent, builds a task DAG, invokes tools and agents through a communication layer, and aggregates results. The task lifecycle model gives each sub-task states such as CREATED, READY, DISPATCHING, IN PROGRESS, COMPLETED, FAILED, RETRY SCHEDULED, and ERROR. On that basis, 17 host-agent properties and 14 task-lifecycle properties are expressed in temporal logic and grouped into liveness, safety, completeness, and fairness, making deadlock prevention, dependency ordering, and validated invocation first-class verification targets (Allegrini et al., 15 Oct 2025).

Explainable routing extends the same logic to model selection. Topaz builds skill-based capability profiles from benchmark performance, derives task requirement profiles, and routes each subtask by explicitly weighing skill-match against normalized cost. It then emits developer-facing natural-language explanations grounded in the actual routing trace, rather than post-hoc rationales. This makes it possible to distinguish “intelligent efficiency” from silent budget-driven degradation in routed agentic workflows (Okamoto et al., 4 Apr 2026).

Taken together, these works frame an architectural version of Agentic CLEAR: agent systems should expose what capabilities exist, which tasks are declared, how routing and orchestration were computed, and which formal properties are expected to hold.

5. Learning, collaboration, and systems behavior

Agentic CLEAR is not only about static structure; it also concerns how agents learn to judge, delegate, and verify. Agentic Critical Training recasts agent learning from imitation of expert actions to reinforcement learning over pairwise action judgments. The model is rewarded for identifying the better action among alternatives, rather than for imitating pre-written reflections. Across ALFWorld, WebShop, and ScienceWorld, ACT improved by an average of 5.07 points over imitation learning and 4.62 points over reinforcement learning, while also improving general reasoning benchmarks without reasoning-specific training data (Liu et al., 9 Mar 2026).

In multi-agent communication settings, NetGPT offers a closely related picture. Its core LLM can either reason internally or delegate subtasks to domain-specialized agents through action-oriented communication. Training uses masked loss against external-agent uncertainty, entropy-guided exploration, and multi-objective rewards that jointly capture task quality, coordination efficiency, and resource constraints. The result is a policy that learns when and how to collaborate instead of invoking agents indiscriminately (Yu et al., 31 Jan 2026).

A different but compatible instance is the Agentic Verifier for competitive coding. There, the verifier is not a static judge but a tool-using agent that searches for discriminative test inputs through multi-turn interaction with a code execution environment. Rather than blindly sampling tests, it iteratively refines an input generator to expose behavioral discrepancies among candidate programs, yielding up to +10–15% absolute gains in Best@K accuracy (Ma et al., 4 Feb 2026).

At the systems level, agentic workloads also impose characteristic serving constraints. ReAct-style executions are not merely long-prompt workloads: with effective context caching, most input tokens are reused across turns, making execution decode-dominated while increasing dependence on long-lived KV-cache state. Tool use also follows a temporal structure, shifting from read/explore behavior early in execution to execute/write behavior later (Yuan et al., 25 May 2026). A plausible implication is that an Agentic CLEAR deployment must treat persistent context state, repeated model re-entry, and tool-phase dynamics as operational design constraints rather than afterthoughts.

6. Governance, risk, and accountability

Governance-oriented work extends Agentic CLEAR from evaluation and architecture into risk management. The Agentic Risk & Capability framework is explicitly capability-centric: it treats components, system design, and capabilities as the three primary sources of risk, then maps them to a Risk Register and technical controls. The framework enumerates 46 named risks across elements such as LLMs, tools, internet access, code execution, file management, and system management, and rates them by impact and likelihood before selecting Level 0, 1, or 2 controls (Khoo et al., 22 Dec 2025).

TessPay provides a more concrete infrastructure instance. It implements a “Verify-then-Pay” architecture for agentic commerce with a control-and-verification plane and a separate settlement plane. User intent is captured as mandates, permissions are scoped through Agentic JWTs, funds are locked in escrow, and settlement occurs only when Proof of Task Execution satisfies verification predicates. The full transaction lifecycle is preserved in a tamper-evident audit trail, making accountability legible across discovery, execution, verification, and payment (Goenka et al., 30 Jan 2026).

Legal analysis of agentic AI complicates this picture further. Agentic systems are described as exhibiting stochastic, dynamic, and fluid autonomy, producing co-evolutionary human-machine interactions in which contributions become “irreducibly entangled.” The resulting “unmappability” means that specific creative or operational contributions often cannot be reliably attributed to a human or the machine. The proposed response is “functional equivalence”: legal and policy frameworks may need to assign rights and duties at the level of the combined human-agentic process rather than through fine-grained post hoc attribution (Mukherjee et al., 5 Apr 2025).

One recurring misconception is that better logging alone resolves accountability. These governance papers suggest otherwise. Logs, proofs, and registries help, but responsibility allocation still depends on capability scoping, formal controls, escrowed settlement conditions, human oversight thresholds, and the limits of post hoc attribution (Khoo et al., 22 Dec 2025).

7. Domain adaptations and institutional adoption

In higher education, Agentic CLEAR is framed as an ecosystem-level design principle. The proposed architecture includes learning agents, teaching agents, institutional agents, and inclusion agents connected through a coordination layer and a shared data-and-knowledge layer. The thematic analysis identifies four dominant gaps in current educational AI: task-specific fragmented tools, transition from single-agent to multi-agent systems, limited cross-functional integration, and insufficient focus on inclusivity and accessibility. In this setting, Agentic CLEAR emphasizes coordinated planning, learner-centeredness, equity, accessibility, and resilient governance (Sudarshan et al., 14 May 2026).

Organizational transition work translates similar ideas into deployment practice. A pragmatic framework for transition to agentic AI emphasizes domain-driven use case identification, systematic delegation of tasks to agents, AI-assisted construction of agentic workflows, and small, AI-augmented teams working closely with business stakeholders. Its human-in-the-loop operating model keeps individuals as orchestrators of multiple AI agents, enabling scalable automation while maintaining oversight, adaptability, and organizational control (Bandara et al., 27 Jan 2026).

Smart-transportation research points to a further extension. A recent survey on multi-weather restoration for intelligent transportation systems identifies mixed or compound degradation, real-time deployment, and agentic AI frameworks as future directions. This suggests that Agentic CLEAR is beginning to function not only as an evaluation or governance concept, but also as a systems-integration pattern for perception, restoration, and adaptive tool use in safety-critical environments (Galshetwar et al., 10 Oct 2025).

Across these domains, the common thread is not a fixed acronym but a stable set of requirements: autonomous systems must be observable at multiple levels, explicitly structured, cost- and risk-aware, auditable in their decisions, and aligned with the human institutions that deploy them.