On-the-Fly Agent Construction

• On-the-fly agent construction is a dynamic method that builds specialized autonomous agents using real-time context and live data to overcome the limitations of static designs.
• It employs meta-planning, reflection, and symbolic synthesis to instantiate and optimize agent toolsets and reasoning loops tailored to current task demands.
• Empirical results show significant performance boosts in areas like penetration testing, software engineering, and ad keyword optimization compared to traditional static frameworks.

On-the-fly agent construction refers to the real-time, context-aware assembly or adaptation of autonomous agents (or agent scaffolds) at runtime, as opposed to relying on a set of static, predesigned, or pre-trained agent configurations. This paradigm leverages live information about the current task, environment, or user query to instantiate specialized reasoning, tool-integration, or behavioral schemata, achieving superior flexibility and improved empirical performance in domains as diverse as automated penetration testing, software engineering, ad keyword optimization, multi-agent orchestration, formal synthesis, and cognitive social modeling.

1. Motivation and Theoretical Basis

Conventional agent-based systems are typically engineered in advance either as generic agents with broad but shallow expertise, or as highly specialized configurations, each narrowly optimized for specific scenarios. This dichotomy leads to sub-optimality: generic agents lack requisite situational priors or tool sophistication, whereas bespoke agents incur a prohibitive design burden and are brittle with respect to novel task contexts or objectives (Huang et al., 11 Jan 2026, Xia et al., 17 Nov 2025). On-the-fly agent construction solves these issues by enabling the autonomous and adaptive synthesis of agent prompts, toolsets, and reasoning strategies, based explicitly on target-specific reconnaissance, environmental context, or live data streams. As such, it circumvents the combinatoric explosion inherent in hand-crafting an agent for every plausible task-context pair (Huang et al., 11 Jan 2026), while also supporting continuous improvement and dynamic scaffold evolution (Xia et al., 17 Nov 2025).

Theoretical frameworks for on-the-fly agent construction arise in both classic planning/synthesis domains—where the construction of reactive controllers is interleaved with environment model decomposition (Li et al., 6 Aug 2025)—and in contemporary LLM-centric workflows, where the scaffold/prompt/toolset can be mutated or extended via self-reflection and meta-planning mechanisms (Xia et al., 17 Nov 2025, Chen et al., 3 Jul 2025).

2. Methodologies and Architectures

Approaches to on-the-fly agent construction are domain-dependent but share certain architectural motifs:

• Meta-Planning and Scaffold Synthesis: A meta-planner orchestrates the agent construction loop, dynamically scoring candidate strategies or agent types as a function of observed context or system state (Huang et al., 11 Jan 2026). In multi-agent frameworks, a designer LLM synthesizes the initial finite state machine (FSM) structure from a high-level task description, which can then be optimized or refined iteratively (Zhang et al., 30 Jul 2025).
• Reflection and Tool Discovery: Agents interleave their core reasoning/action loops with explicit “reflection” phases to determine whether additional custom tools, prompt augmentations, or behavioral modules could accelerate task progress or resolve current bottlenecks (Xia et al., 17 Nov 2025). In OMS, a self-reflection loop guides the replacement or retention of generated outputs in response to multi-objective KPI feedback (Chen et al., 3 Jul 2025).
• On-the-Fly Model Synthesis: For theory-of-mind and planning tasks, symbolic world and agent models are synthesized from language and perceptual inputs, enabling Bayesian inverse planning or game-theoretic synthesis to be performed “on-the-fly” for each novel scenario (Ying et al., 20 Jun 2025, Li et al., 6 Aug 2025).

Table: Core On-the-Fly Agent Construction Methodologies

Paradigm	Agent Construction Mechanism	Key Domain
Meta-planning (LLM)	Dynamic prompt/toolset/loop synthesis	Penetration testing (Huang et al., 11 Jan 2026)
Scaffold self-evolution	Reflection-driven tool augmentation	Software engineering (Xia et al., 17 Nov 2025)
FSM auto-gen	LLM-spec → FSM → state merging	Multi-agent systems (Zhang et al., 30 Jul 2025)
Compositional synthesis	Live symbolic/game decomposition	LTLf/reactive synthesis (Li et al., 6 Aug 2025)
Symbolic model syn.	Language/vision → env./agent models	ToM/social reason. (Ying et al., 20 Jun 2025)

3. Formal Models and Algorithms

On-the-fly construction is typically realized algorithmically by interleaving model/context extraction, candidate ranking, and scaffold instantiation:

• PenForge for Penetration Testing: The two-phase workflow comprises (i) reconnaissance (endpoint/parameter/knowledge extraction), yielding context tuple $C = (E, P, K)$, and (ii) sequential candidate attack type scoring using $s_i = \mathrm{ScoreFunction}(a_i \mid C)$, meta-planner selection, and expert agent prompt synthesis. The agent runs an observe–think–act loop until success or resource exhaustion. The procedure is formalized as:

$$\text{For }a_i\in A,\,\, s_i \leftarrow \mathrm{ScoreFunction}(a_i\mid C)$$

$a^* = \arg\max_{a_i\in A} s_i$

followed by context-driven agent instantiation and sequential trial (Huang et al., 11 Jan 2026).

• Live-SWE-agent: Implements an interactive Markov decision process with state, action, and reward functions tailored to software code modification and tool creation. The action set $\mathcal{A}$ includes both $\textrm{EXECUTE}(cmd)$ and $\textrm{CREATE\_TOOL}(name, source)$, with test-passing success as the sole reward. The cumulative solve rate increases with scaffold adaptability (Xia et al., 17 Nov 2025).
• Compositional LTLf Synthesis: Solves $\varphi = \wedge_{i=1}^n \varphi_i$ by incrementally composing agent-winning regions, alternating between “prune-before” and “prune-during” product construction, to support early unrealizability detection and minimize state-space traversal. The formal compositional operator is

$$\mathrm{Awr}_{1..i} = \rho\Big(\rho(\dots \rho(G_{\varphi_1}) \otimes G_{\varphi_2}) \dots \otimes G_{\varphi_i}\Big)$$

where $\rho$ is a pruning operator and $\otimes$ denotes game product (Li et al., 6 Aug 2025).

4. Empirical Results and Performance Analyses

On-the-fly agent construction consistently yields significant empirical advantages over static agent architectures:

• PenForge (CVE-Bench, zero-day): Achieves a 30.0% exploit success rate (12/40, success@5), exceeding generic and pre-crafted agents by factors of 3–12×. Result distribution: Unauthorized Admin Login (4/12), SSRF (4/12), other classes (4/12). Tool misuse—not context gathering—is the primary failure mode (Huang et al., 11 Jan 2026).
• Live-SWE-agent (SWE-bench Verified, SWE-Bench Pro): Solve rate improves to 75.4% (Python) and 45.8% (Pro benchmark), outperforming all contemporary open-source agents. Cumulative solve rate is monotonic in the number of tools dynamically synthesized per issue (SR ≈ 75% at ⟨k⟩ = 3) (Xia et al., 17 Nov 2025).
• MetaAgent: FSM-based, LLM-generated multi-agent teams match or exceed success rates and performance metrics relative to human-designed systems and significantly outperform previous auto-design methods across creative writing, GPQA, machine learning, and software development benchmarks (Zhang et al., 30 Jul 2025).
• OMS (Online/Offline Ad Keyword Generation): Demonstrates state-of-the-art outcomes (+13.8% conversions, –12.1% CPA online) with ablation studies validating the necessity of each module in the on-the-fly loop (Chen et al., 3 Jul 2025).
• LTLf Synthesis (CoSynt Framework): Solves 3149/3380 instances; both prune-before and prune-during strategies offer unique merits depending on decomposition structure, outperforming all baselines (Li et al., 6 Aug 2025).
• LIRAS: On-the-fly model construction achieves Pearson $r \approx 0.8$–$0.9$ correlation with human judgments in social reasoning tasks. Baseline models hover at $r \approx 0.6$ or lower, demonstrating the importance of run-time model synthesis and Bayesian inference (Ying et al., 20 Jun 2025).

5. Representative Instantiations and Applications

• Penetration Testing (PenForge): Per-target agent prompt synthesis, exploitation tool selection, and live adaptation yield superior performance in challenging zero-day vulnerability hunting scenarios (Huang et al., 11 Jan 2026).
• Software Engineering (Live-SWE-agent): On-the-fly custom tool creation, driven by explicit reflection prompts and test-passing metrics, outperforms both static established agents and reinforcement/self-play-trained systems (Xia et al., 17 Nov 2025).
• Ad Keyword Optimization (OMS): Real-time KPI monitoring, agentic clustering, multi-objective ranking, and self-reflective filtering operate without any pre-labeled data or retraining, enabling robust, scalable campaign management (Chen et al., 3 Jul 2025).
• Automated Multi-Agent System Design (MetaAgent): LLM-guided FSM translation, iterative state merging, and synchronous message-passing support robust orchestration for semantically heterogeneous multi-agent tasks (Zhang et al., 30 Jul 2025).
• Reactive Synthesis (CoSynt): Compositional, on-the-fly agent synthesis enables efficient two-player game-solving for complex logical specifications without full DFA instantiation (Li et al., 6 Aug 2025).
• Cognitive Modeling (LIRAS): Language-informed, vision-integrated environments and agent priors are synthesized on the fly to support probabilistic reasoning in theory-of-mind and social inference tasks (Ying et al., 20 Jun 2025).

6. Limitations, Open Challenges, and Future Prospects

Despite their flexibility, on-the-fly agent construction frameworks share several limitations:

• Tool Usage and Action Missteps: Incorrect tool selection or configuration remains the dominant failure mode, indicating the need for richer, context-specific action or API demonstration (e.g., auto-generated usage snippets) (Huang et al., 11 Jan 2026).
• Cold-Start or History Accumulation: Early-stage agents may suffer from noise or lack of informative priors until feedback accumulates (OMS, Live-SWE) (Chen et al., 3 Jul 2025, Xia et al., 17 Nov 2025).
• Combinatorial Complexity: While pruning, selective expansion, and minimization are used aggressively, worst-case state-space (e.g., in LTLf synthesis) remains doubly-exponential (Li et al., 6 Aug 2025).
• Static Meta-Controllers: Many systems (MetaAgent, PenForge) currently fix the controlling FSM or meta-planner architecture after initial construction or merge phases; real-time adaptation to task drift is an area for future work (Zhang et al., 30 Jul 2025, Huang et al., 11 Jan 2026).
• LLM Dependence: Performance, cost, and latency are closely tied to the capabilities of the underlying LLM; robustness across LLM versions and architectures must be validated (Xia et al., 17 Nov 2025, Chen et al., 3 Jul 2025).
• Restricted Domain Generality: Some frameworks depend on structured environments (e.g., grid-PDDL for LIRAS), limiting their immediate applicability to complex or unstructured domains (Ying et al., 20 Jun 2025).

Future directions referenced across the literature include: real-time online FSM adaptation; multi-modal agent orchestration; scaffold optimization with quantitative/learned merge criteria; generalized theory-of-mind inference; domain-specific retrievers for tool usage; and integrating explainable agent rationale with human-in-the-loop workflows (Zhang et al., 30 Jul 2025, Chen et al., 3 Jul 2025, Li et al., 6 Aug 2025, Huang et al., 11 Jan 2026).

7. Significance and Broader Impact

On-the-fly agent construction represents a paradigm shift towards fully dynamic, context-internalized, and feedback-driven agentic computation. By replacing inflexible, precompiled agent libraries with architectures capable of synthesizing and evolving critical components—reasoning modules, toolchains, behavioral policies—at runtime, this approach enables greater adaptability, improved empirical outcomes across complex, unstructured, or changing environments, and seamless integration of external knowledge and self-assessment mechanisms. Its impact spans autonomous red-teaming, self-improving software development, scalable marketing optimization, multi-agent orchestration, formal synthesis, and even computational cognitive science, representing a foundational advance in agent design theory and practice (Huang et al., 11 Jan 2026, Xia et al., 17 Nov 2025, Zhang et al., 30 Jul 2025, Chen et al., 3 Jul 2025, Li et al., 6 Aug 2025, Ying et al., 20 Jun 2025).