RunAgent: Interpreting Natural-Language Plans with Constraint-Guided Execution
| Authors | Arunabh Srivastava et al. |
| Year | 2026 |
| Field | Machine Learning |
| arXiv | 2605.00798 |
| Download | |
| Categories | cs.LG, cs.CL, cs.MA |
Abstract
Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a multi-agent plan execution platform that interprets natural-language plans while enforcing stepwise execution through constraints and rubrics. RunAgent bridges the expressiveness of natural language with the determinism of programming via an agentic language with explicit control constructs (e.g., \texttt{IF}, \texttt{GOTO}, \texttt{FORALL}). Beyond verifying syntactic and semantic verification of the step output, which is performed based on the specific instruction of each step, RunAgent autonomously derives and validates constraints based on the description of the task and its instance at each step. RunAgent also dynamically selects among LLM-based reasoning, tool usage, and code generation and execution (e.g., in Python), and incorporates error correction mechanisms to ensure correctness. Finally, RunAgent filters the context history by retaining only relevant information during the execution of each step. Evaluations on Natural-plan and SciBench Datasets demonstrate that RunAgent outperforms baseline LLMs and state-of-the-art PlanGEN methods.
Engineering Breakdown
Plain English
RunAgent addresses a critical failure mode in LLM-based automation: large language models struggle to reliably execute multi-step workflows that require deterministic sequencing and constraint satisfaction. The paper proposes a multi-agent execution platform that converts natural-language plans into an agentic language with explicit control constructs (IF, GOTO, FORALL), enabling step-by-step execution with enforced constraints. The system autonomously derives and validates task-specific constraints at each step, bridging the gap between the expressiveness of natural language planning and the determinism required for reliable workflow execution. This approach improves reliability for structured problem-solving tasks where traditional LLM prompting would produce hallucinations or skip critical steps.
Core Technical Contribution
RunAgent's core innovation is an intermediate representation language that sits between natural language and executable code, combining the flexibility of NLP with explicit control flow primitives. Unlike prior work that relies on end-to-end LLM generation with post-hoc verification, RunAgent enforces constraints proactively during execution—each step's outputs are validated against step-specific instructions and autonomously derived task constraints. The system uses a rubric-based evaluation framework where constraints are not hand-coded but derived from the task description and instance-specific context, making it adaptable to new domains without manual constraint engineering. This represents a shift from treating plan execution as a pure generation problem to treating it as a constrained satisfaction problem with LLMs as the reasoning engine.
How It Works
RunAgent operates in three phases: (1) Natural-language plan interpretation—the system parses a high-level plan description and converts it into the agentic language with explicit control flow, maintaining semantic fidelity while adding structure. (2) Constraint derivation—for each step, the system autonomously generates validation constraints based on the step's instruction, the task description, and the current execution context, rather than relying on pre-defined constraints. (3) Stepwise execution with validation—the agent executes each step (calling an LLM or external tool as needed), validates outputs against both syntactic requirements (instruction-specific rules) and semantic requirements (autonomously derived constraints), and either proceeds to the next step or triggers remediation (retry with feedback, backtrack, or error handling). The control constructs (IF, GOTO, FORALL) allow conditional branching, loops, and dynamic plan modification based on execution results, enabling the system to handle variability in task instances.
Production Impact
This approach directly addresses a widespread pain point in production LLM systems: unreliable multi-step execution in workflows like data pipelines, customer support automation, code generation, and scientific research assistants. Production systems using RunAgent would see dramatically lower error rates on structured tasks because constraint violations are caught immediately rather than propagating through the workflow; for example, a data extraction pipeline could enforce that all required fields are populated before moving to the next step. The trade-off is increased latency (multiple validation rounds per step and constraint derivation overhead) and compute cost (additional LLM calls for constraint generation and validation), but this is worthwhile for high-stakes workflows where errors are expensive. Integration requires converting existing plans into the agentic language (one-time engineering effort) and setting up the constraint derivation subsystem, which would likely be built as a service layer around existing LLM APIs rather than requiring model retraining.
Limitations and When Not to Use This
RunAgent assumes that task constraints can be effectively derived from natural-language descriptions, which may fail on ambiguous, adversarial, or highly domain-specific problems where constraint inference requires deep expertise. The paper does not address computational efficiency—the overhead of deriving constraints at every step could make this impractical for latency-sensitive applications or large-scale batch processing. The approach relies on LLM reliability at two points (plan execution and constraint validation), so systematic LLM biases or hallucinations in constraint reasoning could cascade through the system; the paper does not deeply explore failure modes when the LLM's constraint derivation itself is incorrect. Additionally, the method appears to work best for well-defined, decomposable tasks; open-ended creative tasks or problems requiring significant emergent reasoning may not fit the constraint-based execution model.
Research Context
RunAgent builds on a growing body of work on LLM-based planning and execution (e.g., LangChain, AutoGPT, hierarchical planning) but distinguishes itself by introducing explicit structural constraints rather than relying on in-context prompting alone. It relates to formal verification and program synthesis research, particularly work on constraint-guided code generation and execution trace validation. The paper implicitly advances the field toward hybrid systems that combine symbolic reasoning (explicit control flow, constraints) with neural reasoning (LLM agents), a direction also explored in neurosymbolic AI and differentiable reasoning. This work opens questions about how to automatically learn or transfer constraint templates across domains, how to scale constraint derivation to very long plans, and how to integrate this with reinforcement learning for plan optimization.
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