LLM Agents for Autonomous Tool Management

• LLM agent tool-making is a paradigm where language models autonomously design, update, and verify computational tools for dynamic, open-ended tasks.
• Frameworks integrate semantic retrieval, code synthesis, and MDP-based optimization to perform CRUD operations and iterative debugging on tool libraries.
• Hierarchical architectures decouple planning from tool execution, enhancing credit assignment and improving task success rates in complex domains.

LLM agents making agent tools constitute a paradigm wherein LLM-powered agents autonomously create, manage, adapt, and optimize their own tool ecosystem—spanning retrieval, construction, orchestration, and verification. This enables LLM agents not only to dynamically use external computational capabilities in open-ended tasks but also to synthesize, update, or retire those capabilities without direct human engineering. The field thereby bridges classical software agent composition with the flexibility and on-the-fly learning made possible by modern generative models.

1. Formal Definitions and Theoretical Foundations

LLM agents are typically defined as systems where a LLM acts as the central reasoning engine, invoking pre-defined or dynamically constructed tools to extend its functional scope. In unified agent architectures, such as the LLM-Agent-UMF, agents are expressed as the tuple $\mathcal{A} = (C, L, \mathcal{T})$, where $C$ is the set of core-agents (controllers), $L$ the LLMs, and $\mathcal{T}$ the set of tools (APIs, compute modules, environments) (Hassouna et al., 17 Sep 2024). Each core-agent module is further decomposed into Planning, Memory, Profile, Action, and Security, separating reasoning over tool choice from direct tool execution.

In agent tool autonomy, an LLM not only selects from an existing $\mathcal{T}$ but is itself agentic in (a) discovering external APIs (e.g., via Model Context Protocol [MCP] servers), (b) generating new tool code or wrappers, and (c) performing CRUD (create, read, update, delete) operations on tool libraries (Lumer et al., 9 May 2025, Wölflein et al., 17 Feb 2025, Ocker et al., 31 Jul 2024).

The tool creation, invocation, and evolution pipeline in an agent is effectively modeled as a Markov Decision Process (MDP) where tool creation primitives are formal actions, and the available toolset enriches the state space over time (Cheng et al., 18 Nov 2025).

2. Architectures and Frameworks for Tool-Autonomous Agents

Several recent architectures operationalize LLM agents in the context of agent tool management and synthesis:

• ToolMaker (Wölflein et al., 17 Feb 2025): This agentic framework ingests a task description, a GitHub repository, and argument signatures, automatically installs dependencies, and generates a LLM-compatible Python function as a tool that is validated via closed-loop self-correction. Key stages include environment setup, multi-stage code generation, and iterative debugging. ToolMaker achieved a task success rate of 80% (12/15) and a 94% unit-test pass rate on real scientific code tasks, surpassing domain SOTA software agents such as OpenHands.
• Tulip Agent (Ocker et al., 31 Jul 2024): The CotTulipAgent and AutoTulipAgent expose full CRUD over a vector-store-backed tool library. Agents recursively perform semantic (embedding-based) search to identify relevant tools, create or update tool definitions on-the-fly with code-generating LLM backends, and remove obsolete entries. Retrieval is recursive: subtasks are decomposed and individually mapped to candidate tools.
• ScaleMCP (Lumer et al., 9 May 2025): Leverages MCP servers as the single source of truth for tools (each specified with name, description, params, example queries). ScaleMCP performs periodic auto-synchronization with the MCP registry, supports vector-based, lexical, and reranking retrieval (notably with the Tool Document Weighted Average, TDWA, strategy), and allows agents to dynamically add or remove tools in "memory." Performance indicates agent-driven retrieval and tool binding improves multi-turn completion and tool correctness (Task Completion >85%, Tool Correctness up to 54% for top LLMs).
• Agent-as-Tool (Zhang, 2 Jul 2025): Employs a hierarchical split between high-level reasoning (Planner) and tool execution (Toolcaller). The Planner agent decides when/what tool to invoke, and a separate Toolcaller agent interfaces with the tool, returning sanitized, context-appropriate observations. This decoupling substantially reduces noise (irrelevant or brittle tool outputs) in the reasoning loop and improves credit assignment in RL-based fine-tuning.
• Agent-R1 (Cheng et al., 18 Nov 2025): Provides a modular RL training framework for LLM agents over tools, with extensible action space allowing agents to define new tool-creation primitives. By modeling tool definition as a valid MDP action, and extending rewards and transition functions to reflect changing tool sets during training, Agent-R1 supports fully end-to-end optimization of agent-tool interaction.

3. Algorithms and Mechanisms for Tool Creation and Management

LLM agent tool creation and management is realized through a combination of semantic retrieval, code synthesis, validation, and orchestration:

• Semantic Tool Retrieval: Agents employ embedding-based or hybrid (vector + reranker) search to locate potentially relevant tools given the current subtask. Techniques such as TDWA optimize retrieval embeddings by weighing name, description, params, and synthetic queries (Lumer et al., 9 May 2025). Recursive search, as in Tulip Agent, allows decomposition into finer-grained tool queries when no suitable match is found.
• Tool Synthesis and Update: When the tool library cannot satisfy a subgoal, agent frameworks such as ToolMaker or AutoTulipAgent generate new tool code using LLMs prompted with structured task information and repository APIs. Synthesized code is validated syntactically and, where possible, functionally using unit tests or exemplar invocations (Wölflein et al., 17 Feb 2025, Ocker et al., 31 Jul 2024). The tools, once validated, are indexed (vector embedding) for future retrieval.
• Environment and Dependency Handling: Automated environment preparation is essential. ToolMaker’s install_repository agent infers and scripts dependency installation by reading repository metadata, utilizing bash, and checkpointing the environment in containers, thereby standardizing tool execution contexts.
• Auto-Synchronization and Lifecycle Management: Using periodic hashing and vector indexing, agents maintain alignment with dynamic external tool registries (via MCP servers), ensuring up-to-date availability, and performing CRUD on obsoleted or updated tools (Lumer et al., 9 May 2025).
• Closed-loop Self-Correction: After tool code generation, agents run candidate implementations, evaluate output plausibility, diagnose error traces, and iteratively refine the tool, appending concise iteration summaries to the context for full traceability (Wölflein et al., 17 Feb 2025).

4. Reinforcement Learning and Credit Assignment in Tool-Making Agents

Agent frameworks have extended RL methodologies to the context of agent tool synthesis, orchestration, and invocation:

• Credit Assignment with Hierarchical Decomposition: Decoupled Planner/Toolcaller architectures restrict the RL optimization signal to high-level reasoning and tool-selection decisions, masking tool output content in credit assignment to prevent spurious gradients (Zhang, 2 Jul 2025).
• End-to-End On-Policy Optimization: PPO, GRPO, and other policy-gradient algorithms are employed in frameworks like Agent-R1 to optimize over token generation (both reasoning and tool calls). Action and advantage masks are applied to focus learning on agent-originated decisions, with intermediate process rewards assigned for correct tool invocation and formation (Cheng et al., 18 Nov 2025).
• Meta-actions for Tool Creation: The action space of the agent is extended to include tokens or structured calls representing tool creation actions (e.g., create_tool(name, schema, code)), which, upon successful execution, add to the agent’s toolset in the evolving MDP environment (Cheng et al., 18 Nov 2025). Intermediate rewards penalize syntactic or semantic errors and incentivize robust, safe tool definitions.

5. Evaluation Protocols and Empirical Findings

Evaluation involves synthetic and real-world benchmarks spanning software engineering, open-domain QA, mathematics, robotics, and chemistry:

Framework	Tool Creation Success	Retrieval/Invocation Accuracy	Unit-Test Pass Rate	Domain
ToolMaker	80% (15 tasks)	N/A	94%	Biomedical Science
ScaleMCP	Up to 54% (Tool Corr)	>85% (Task Completion, LLMs)	N/A	Finance (MCP APIs)
Tulip Agent	N/A	Up to 55% on hard math tasks	N/A	Mathematics, Robotics
Agent-as-Tool	+4.8% EM vs. Search-R1 (Bamboogle)	63.2% EM/75.2% CEM (w/ RL)	N/A	Multi-hop QA
Agent-R1	PPO: 0.41–0.54 EM (Hotpot/2WikiMultihop), GRPO: Best average	Substantial over RAG baselines	N/A	Multi-hop QA

ToolMaker outperforms OpenHands by 60 percentage points on scientific code tool creation (Wölflein et al., 17 Feb 2025). In large tool ecosystems, vector+reranker retrieval pipelines improve recall and MAP over vector-only or BM25 approaches (Lumer et al., 9 May 2025). Recursive search and decomposition, especially when combined with Chain-of-Thought priming, increase correctness on complex mathematics tasks (Ocker et al., 31 Jul 2024).

6. Best Practices, Challenges, and Limitations

Key design principles for agents autonomously handling tools include:

• Dynamic tool invocation: Agents should first predict $P(\text{"tool helps"} \mid \text{prompt})$ before issuing a tool call, avoiding unnecessary context switching (Yu et al., 11 Nov 2024).
• Cognitive load partitioning: Hierarchical or multi-agent splits (e.g., Planner vs. Toolcaller) reduce reasoning distractions and clarify credit assignment (Zhang, 2 Jul 2025).
• Verification and cross-validation: Agents must reconcile or self-debug conflicting tool outputs, especially in safety-critical settings (Yu et al., 11 Nov 2024, Wölflein et al., 17 Feb 2025).
• Modular, domain-specialized toolsets: Narrow, high-precision tools outperform bloated or generic tool libraries in specialized domains (Yu et al., 11 Nov 2024).
• Extensibility and Reproducibility: Modular codebases (∼500 LOC in ToolMaker) and standard interface contracts for tools (name, description, params, execute()) promote community extensibility (Wölflein et al., 17 Feb 2025, Cheng et al., 18 Nov 2025).

Key challenges and limitations persist: (i) agent tool-autonomy presumes high-quality source code/documentation and is brittle to poorly structured repositories; (ii) in open-ended domains, insufficient coverage in tool libraries can bottleneck agent performance; (iii) safety and oversight requirements preclude fully unsupervised tool activation in critical environments (Wölflein et al., 17 Feb 2025).

7. Future Directions and Open Problems

Emerging directions include:

• Automated algorithm extraction from paper text (not only repos) and formal specification of new tools (Wölflein et al., 17 Feb 2025).
• Integration of domain ontologies and semantic constraints during tool creation (Wölflein et al., 17 Feb 2025).
• Formal verification of tool-agent pipelines, especially for high-assurance domains (healthcare/finance) (Hassouna et al., 17 Sep 2024).
• Hybrid RL+Supervised training regimes for meta-actions (creating, updating, and retiring tools) (Cheng et al., 18 Nov 2025).
• Distributed multi-core-agent orchestration with dynamic leadership and intra-agent buses for increased robustness and scalability (Hassouna et al., 17 Sep 2024).

Altogether, the synthesis of agentic LLM reasoning and dynamic tool lifecycle management enables fully autonomous, adaptable, and scalable scientific workflows and problem-solving pipelines, with ongoing research pushing the boundaries of agent tool intelligence, safety, and extensibility.