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Review Open access Aug 2026

From Language Models to Agentic AI: A Survey of Autonomous, Action-Enabled, and Collaborative LLM Agents

A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.

Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al. · 0 citations
Preprint Aug 2026

Harness Engineering for Predictable Agentic Systems: An Empirical Study of Deterministic Execution Constraints

Large Language Model (LLM) based agents exhibit substantial run-to-run execution variance even when given identical tasks and tools -- acceptable for exploratory use but unacceptable in regulated domains such as finance and compliance. We study harness engineering: wrapping an agent in a deterministic execution layer (finite-state control, forced tool selection, output validation, bounded retry, and structured planning) and measuring its effect on execution determinism and task success. Across two synthetic tasks (finance and legal) and two open-weight models (Qwen-2.5-7B-Instruct, Gemma-3-27B), a first-pass harness produces a mixed result: it significantly improves reproducibility in one of four model-task cells, significantly degrades it in two, and has no effect in the fourth. A trace-level diagnostic finds the cause: once tool sequence, state sequence, and output are already highly consistent, an unconstrained free-text planning step becomes the dominant remaining source of variance. Adding Structured Planning -- validating the plan against a fixed schema before any tool is invoked -- eliminates the effect entirely: three of four cells reach a Reproducibility Rate and Determinism Index of 1.000 at N=100, and task success rises to 100% in three of four cells. The gain is not free: token cost falls in every cell, but latency shows a genuine, sample-size-robust split by model -- one becomes faster under the constraint, the other markedly slower. We argue harness engineering is a distinct, effective discipline for agent reliability, but its cost is model-dependent and must be measured, not assumed.

SA Dhage · 0 citations
Preprint Aug 2026

AutoMOOSE: Use Case and Logical Views of Agentic Phase-Field Simulation Software

AutoMOOSE is an agentic software framework that converts a natural-language request into an executed, screened, and interpreted MOOSE phase-field simulation. Here, we deploy AutoMOOSE as a agentic software, complementing our prior work which focused on development of the agentic tool. We describe our software framework and architecture through Use Case and logical views of the 1+5 architectural-views model, covering its user roles, component structure, six-agent pipeline, physics plugin layer, Model Context Protocol interface, and screening/falsification/recovery loop. Our architecture separates physical falsification from automatic repair, so corrected simulations remain inspectable and must be re-admitted before acceptance. We focus on software design, extensibility, interoperability, and reuse of the AutoMoose framework for broad utilization in multiphysics materials design problems.

Sukriti Manna, Henry Chan, Subramanian K. R. S. Sankaranarayanan · 0 citations
Preprint Jul 2026

Do AI Agents Know When a Task Is Simple? Toward Complexity-Aware Reasoning and Execution

Large language model (LLM) agents increasingly automate multi-step engineering and informatics workflows, yet they rarely ask how much effort a task actually requires. They often follow a maximum-context-first strategy--re-reading files and dependencies they have already seen--turning a one-line edit into a small code-base audit. We argue the missing capability is task-aware execution-scope estimation: judging a task's difficulty, the information it truly needs, and the shortest reliable path before committing budget. We formalize minimum-sufficient execution and the Agent Cognitive Redundancy Ratio (ACRR), and propose E3 (Estimate, Execute, Expand): the agent estimates an initial operating point, executes a minimum viable path, and expands scope only when verification fails. On MSE-Bench--a deterministic benchmark of 121 edits in a capability-controlled simulator--E3 matches the strongest baseline's 100% success while cutting cost by 85%, tokens by 91%, and inspected files by 92%, and further beats a strong adaptive retrieval baseline by 16%; the gains survive held-out instruction wording and essentially every cost weighting. A companion real-model harness (LLM-Case) corroborates the effect on a live gpt-4o agent editing a real open-source library, with every candidate patch graded by actually running the project's real pytest suite against a measured oracle: the over-reading is milder but real, and E3 is the leanest and fastest policy at comparable task success--its one shortfall a provider rate-limit, not a wrong edit. We frame this as a controlled probe of execution redundancy, not a measurement of any deployed agent, and position task-aware execution as a step toward engineering-grounded AI (EGAI)--agents whose effort is anchored in the engineering reality of the task. We release the framework and benchmark.

J. Yin, Xinyu Feng · 1 citation
Preprint Aug 2026

MADE: Belief-Driven Dual-Agent Coordination for Autonomous Model Deployment

This work introduces Model Automated Deployment Engine (MADE), a dual-agent coordination system that iteratively constructs and validates the deployment artifacts, updates its deployment belief based on execution feedback, and revisits invalid upstream artifacts until the model is successfully served as a ready-to-call API that can then be used by other agents.

Yicheng Liu, Bolin Zhang, Weiran Liu et al. · 0 citations
Open access Aug 2026

Tool-Augmented Language Agents with Iterative Self-Critique for Complex Task Planning

but struggle when confronted with multi-step, complex task planning that requires interaction with external environments. This paper investigates the architecture, implementation, and efficacy of tool-augmented language agents enhanced with iterative self-critique mechanisms. By integrating external application programming interfaces, structured databases, and computational engines, these agents transcend isolated text generation, evolving into active systems capable of executing concrete actions. However, naive tool utilization often results in cascading errors during prolonged execution trajectories. To mitigate this, we introduce an iterative self-critique framework where the agent continuously evaluates its own outputs, identifies logical fallacies or execution failures, and dynamically recalibrates its plan. This research details a comprehensive methodological framework, formalizing the probabilistic decision-making and critique generation processes. Empirical evaluations across simulated complex environments demonstrate that the proposed architecture significantly improves task success rates, minimizes superfluous tool invocations, and enhances error recovery. The findings indicate that integrating reflective cognition paradigms with modular toolsets is essential for deploying autonomous language agents in high-stakes, real-world applications.

Mabel Kwok · 0 citations