Multi-step Tool Invocation Planning: Foundations, Architectures, Challenges, Applications, and Future Directions

Introduction: Defining Multi-step Tool Invocation Planning

Multi-step tool invocation planning, also known as multi-step tool use or multi-step reasoning, represents an advanced technique that enables Artificial Intelligence (AI) models, particularly Large Language Models (LLMs), to dissect complex tasks into a series of smaller, manageable steps . This method then allows these models to invoke external tools to execute each step, effectively mirroring human problem-solving capabilities by processing information sequentially, applying logic, and dynamically adapting to achieve a final solution 1. By leveraging this approach, AI models can extend their capabilities beyond mere information retrieval to actively interact with a diverse range of external resources, including search engines, Application Programming Interfaces (APIs), functions, and databases 2.

Core Concepts and Theoretical Underpinnings

The fundamental principle governing multi-step tool invocation planning is a cyclic process often referred to as an "Agent Loop," which encompasses planning, action, observation, and reflection . Key elements contributing to this paradigm include:

• Planning Stage: Upon receiving a user request, the model first formulates a logical sequence of actions or a comprehensive plan, identifying which tools to utilize, their order of execution, and necessary parameters . This plan can be a detailed multi-step execution plan complemented by reasoning for each action 3.
• Tool Definitions: External functionalities, known as tools, are explicitly defined to allow the agent to interact with the real world. Examples range from file utilities, web search, and database queries to Python functions and email senders 4. These tools serve as the means to execute specific actions outlined in the plan .
• Execution Stage: The model proceeds to carry out the formulated plan by repeatedly executing actions using the appropriate tools . In sophisticated architectures like LLMCompiler, tasks can even be scheduled and executed in parallel once their dependencies are satisfied, often represented as a Directed Acyclic Graph (DAG) 5.
• Observation/Reflection: After each action's completion, the model observes the results and reflects on the progress made, assessing if the plan requires refinement or if it possesses sufficient information to generate a final response 2. This critical phase involves analyzing findings, determining necessary adjustments to the plan, and deciding the subsequent step 2.
• Incremental Planning and Refinement: Approaches such as Pre-Act exemplify this by generating an initial comprehensive multi-step plan, where steps are executed sequentially. Each subsequent step incorporates context from previous steps and tool outputs, allowing for continuous refinement of the plan after every execution 3. This iterative incorporation ensures both coherence and adaptability throughout the task 3.
• Multi-step Reasoning: Agents are designed to break down complex tasks into smaller sub-tasks, a process that mirrors human cognitive problem-solving. Each step builds upon the preceding one, enabling LLMs to address intricate challenges that extend beyond simple question-answering .
• Planning Prompts: These are explicit instructions crafted to guide the agent's thought process before action. They typically outline an iterative loop such as "Think → Act → Observe → Next Step" and necessitate a step-by-step plan for the agent to follow 4.

Distinction from Related AI Paradigms

Multi-step tool invocation planning distinguishes itself significantly from other AI paradigms, offering enhanced capabilities for complex problem-solving.

1. Single-step Tool Use: Unlike multi-step planning, single-step tool use allows a model to invoke multiple tools but only within a single step 2. A crucial limitation is its inability to execute a sequence of steps or utilize results from one tool call in a subsequent step 2. In contrast, multi-step planning explicitly enables sequential execution, leveraging results from prior steps to inform future actions and dynamically adapt the plan .

2. Traditional AI Planning (e.g., ReAct Agents): Traditional ReAct (Reasoning + Action) agents, which operate through a "thought, act, observation" loop, also use LLMs as problem-solvers . However, a key distinction for multi-step planning is that ReAct typically necessitates an LLM call for each tool invocation and plans only for one sub-problem at a time 5. The reasoning in ReAct often focuses solely on the immediate action, which can be insufficient for complex tasks requiring a sequence of interdependent actions 3. Multi-step tool invocation planning, particularly evident in "Plan-and-Execute" architectures, distinctly separates a larger LLM-powered "planner" from the tool execution runtime. This separation facilitates an initial comprehensive plan, thereby reducing the necessity to consult the main LLM after each action, leading to faster execution and potential cost savings through the use of smaller, domain-specific models for sub-tasks 5. Innovations like "Pre-Act" specifically enhance ReAct by generating a multi-step execution plan with detailed reasoning for each action, integrating previous steps and observations to improve action recall 3.

3. Retrieval Augmented Generation (RAG): While tool use, including its multi-step variant, is seen as a natural extension of RAG, it fundamentally goes beyond it 2. RAG primarily empowers models to interact with information retrieval systems, such as vector databases, to fetch relevant information, simplifying prompts by providing context from external knowledge bases . Multi-step tool invocation, however, pushes this capability further by allowing models to act upon retrieved information and engage with a much broader array of tools—beyond just information retrieval—including search engines, APIs, functions, and databases 2.

The concept of autonomous agents and agentic architectures is not novel, with foundational work exploring distributed agents and coordinated systems dating back to earlier efforts such as the Open Agent Architecture by Martin et al. (1999) and the Galaxy Architecture by Seneff et al. (1998) 3. These historical precedents laid groundwork for the sophisticated multi-step planning capabilities seen in contemporary AI systems.

Architectural Components and Methodologies

Multi-step tool invocation planning systems integrate large language models (LLMs) with specialized computational tools, enabling complex, multi-step reasoning and task execution across diverse domains 6. These systems typically adopt an orchestrator-agent pattern, where the LLM functions as a central controller within a cyclical "plan-act-reflect" loop 6.

Architectural Components

The foundational architecture of LLM-assisted tool run frameworks is built upon several interconnected components that facilitate planning, execution, and dynamic adaptation.

1. LLM Backbone/Planning Module: This serves as the core cognitive engine, responsible for natural language understanding and generation, driving reasoning, decision-making, and tool use . It analyzes user queries, decomposes problems into sub-tasks (e.g., textual, numerical analysis), and generates structured plans that specify tool types and their required inputs 6.
2. Tool Executors: These are external processes invoked by the orchestration layer to perform specific functions 6. They encompass various categories:

   Tool Typology	Description
   Evidence-Gathering Tools	Modules for web searches with filters and literature retrieval (e.g., PubMed, Wikipedia APIs) 6.
   Credibility Assessors	Heuristic or data-driven evaluators for source trust or confidence metrics 6.
   Algorithmic/Statistical Tools	Numerical verification engines, domain-specific computation modules for tasks like code analysis, root-cause attribution, strategic planning, or computational First-Order Logic (FOL) evaluators 6.
   Code/API Wrappers	Sandboxed execution frameworks for user-specified or LLM-generated code, alongside dynamic tool generation engines that synthesize and register new tools 6.

3. Memory / Context Management: This component includes persistent storage, often referred to as a "working memory" or "evidence log" . It captures every tool call, input, output, metadata, and timestamp, supporting stateful and auditable reasoning chains . This enables retrieval of intermediate results and manages both short-term (e.g., conversation history) and long-term memory (e.g., vector databases) . In systems like AgentFlow, memory is an explicit and deterministic record, ensuring transparency and controllability of multi-turn decisions 7.
4. Control Cycle / Planning Mechanisms: The LLM issues an action, observes the tool's output, updates its internal state or plan, and proceeds iteratively until all branches resolve 6. This mechanism supports dynamic re-planning in response to conflicting evidence or tool outputs 6. Agentic systems incorporate "planning mechanisms" that sequence actions and allow the agent to "reflect" on its decisions 8.
5. Execution Verifier: Modules, such as those in AgentFlow, evaluate the validity of an execution observation and assess if the accumulated memory is sufficient to resolve the query, yielding a binary verification signal 7.
6. Solution Generator: Upon termination of the multi-step process, this module is responsible for producing the final solution, conditioned on the original query and the accumulated memory 7.

Planning Methodologies and Algorithms

Various planning approaches are employed to enable multi-step reasoning and action in these systems:

• ReAct (Reasoning + Action): A foundational approach that integrates reasoning with action execution, typically following a "thought, act, observation" loop . While effective for simpler tasks, it often struggles with complex, long-term planning due to its focus on single-step reasoning and the requirement for an LLM call for each tool invocation .
• Pre-Act: An enhancement to ReAct, where the system generates a comprehensive multi-step plan with detailed reasoning for each action upfront 3. This plan incrementally incorporates previous steps and tool outputs, refining itself after each step execution 3.
• Plan-And-Execute: This design pattern separates an LLM-powered "planner" from the tool execution runtime 5. The planner generates a multi-step plan, which is then carried out step-by-step by an executor 5. This separation reduces the need to consult the large planner LLM after each action, leading to faster and potentially more cost-effective execution 5.
• ReWOO (Reasoning WithOut Observations): An agent design where the planner generates a plan list consisting of interleaved "Plan" (reasoning) and execution steps (E#) 5. It supports variable assignment, allowing subsequent tasks to reference outputs from previous tasks without requiring re-planning at each step 5. A "worker" executes tasks and assigns outputs to variables, while a "solver" integrates results for the final answer 5.
• LLMCompiler: This methodology focuses on increasing task execution speed by streaming a Directed Acyclic Graph (DAG) of tasks from the planner 5. A "task fetching unit" schedules and executes tasks once their dependencies are met, supporting parallel execution and variable arguments 5. A "joiner" dynamically decides whether to respond or re-plan 5.
• Reinforcement Learning (RL) and Fine-tuning:
  • Outcome-Driven RL: Fine-tuning LLMs to maximize verifiable rewards, which can lead to sophisticated behaviors in self-correction and multi-step deduction 7.
  • Flow-based Group Refined Policy Optimization (Flow-GRPO): An on-policy algorithm used in frameworks like AgentFlow 7. It addresses long-horizon, sparse-reward credit assignment by converting multi-turn optimization into a sequence of tractable single-turn policy updates 7. It broadcasts a single, verifiable trajectory-level outcome reward to every turn and stabilizes learning with group-normalized advantages, allowing the planner to adapt to trajectories shaped by tool calls and verifier signals 7.
  • Curriculum Learning: Involves incremental fine-tuning in stages, such as initial fine-tuning on general datasets for basic agentic capabilities, followed by progressive refinement on proprietary datasets with explicit planning mechanisms and detailed reasoning 3. LoRA (Low-Rank Adaptation) can be employed to preserve learning from previous steps and mitigate catastrophic forgetting 3.
• Retrieval-Augmented Generation (RAG): Agentic RAG enhances traditional RAG by embedding an autonomous agent that intelligently orchestrates which information to retrieve and when to call additional services, extending beyond one-shot retrieval .
• Chain-of-Thought (CoT) Prompting: Enables LLMs to break down complex problems into intermediate steps, a technique leveraged in many planning methodologies .
• Evidence Logging: This involves recording each tool invocation with a step ID, tool name, input, output, and contextual metadata 6. This log is utilized for real-time reasoning (e.g., recalling prior credible sources) and post-hoc inspection to ensure verifiable reasoning chains and reports 6.

Interaction within Multi-step Systems

These components and methodologies interact to create a dynamic problem-solving loop, ensuring adaptive and iterative task completion:

1. Initial Query Analysis: The planning module, driven by the LLM, receives a user query, analyzes it, and determines if external information or actions are necessary .
2. Plan Generation: Based on the query, the planner formulates a multi-step execution plan, detailing sub-goals, specific tools to be used, and their required inputs . This plan can manifest as a sequential process, a tree-of-thought, or a Directed Acyclic Graph (DAG) of tasks with dependencies .
3. Tool Selection and Invocation: The system selects the appropriate tool from its available arsenal and constructs a function call with the necessary parameters, often facilitated by explicit schemas like JSON or OpenAI function call protocols .
4. Execution and Observation: The tool executor invokes the chosen tool, which then returns an observation or result .
5. Memory Update and Verification: The output from the tool is logged in the working memory, which is deterministically updated . A verifier might then assess the validity of the observation and the sufficiency of the current context 7.
6. Reflection and Re-planning: The agent observes the tool's output, reflects on its implications, and integrates this new information into its current state or context . If the outcome deviates from expectations, if a task fails, or if further steps are needed, the planner may dynamically refine the plan, adapt its strategy, or initiate follow-up actions .
7. Iteration or Termination: This loop continues iteratively until the task is complete, a final solution is generated, or a predefined maximum turn budget is reached . Subsequently, the solution generator produces the final response 7.

Key Frameworks

Several frameworks have emerged to build and manage these agentic systems, each offering distinct capabilities:

• LangChain: A popular modular framework designed for workflows where agents maintain state, call external APIs, and chain multiple steps 8.
• CrewAI: An open-source platform specifically for creating multi-agent systems that can delegate tasks and collaborate effectively 8.
• LangGraph and Letta: These frameworks extend LangChain's capabilities by providing visual, graph-based workflow design, which significantly facilitates monitoring and debugging processes .
• AgentFlow: A trainable, "in-the-flow" agentic system that coordinates specialized modules (Planner, Executor, Verifier, Generator) through an evolving memory 7. It directly optimizes its planner inside the multi-turn loop using Flow-GRPO 7.
• ATLASS (Advanced Tool Learning and Selection System): A framework noted for maintaining centralized tool registries with embedding-based semantic retrieval, optimizing tool reuse versus regeneration based on inference cost 6.

These systems collectively emphasize agent modularity, retrieval-augmented generation, explainability, and auditability through rigorous artifact logging 6. However, challenges remain regarding latency, cost, reliability, complexity, and ensuring security and data privacy 8.

Challenges and Limitations

Multi-step tool invocation planning in large language model (LLM) agents, while holding immense promise, introduces a complex array of technical and practical hurdles, fundamental limitations, and open research problems. These challenges range from the granular aspects of task execution and interaction to broader concerns of safety, efficiency, and ethical implications. This section details these significant obstacles that hinder the robust and scalable deployment of such systems.

Major Technical and Practical Hurdles

The implementation of multi-step tool invocation planning faces several significant challenges in its design and operation:

• Task Decomposition and Assignment: Breaking down complex tasks into smaller, manageable pieces and assigning them appropriately to agents or sequential steps is inherently difficult, especially when the dependencies between tasks are unclear 9. This can lead to inefficiencies, duplicated efforts, or critical tasks being overlooked 9. Ambiguity in instructions can cause agents to misunderstand their roles, leading to duplicated work or missed steps 9.
• Workflow Design: Designing an effective workflow that appropriately utilizes agent specializations and coordinates sub-tasks requires careful consideration 10. The workflow must be structured to maximize each agent's unique capabilities and ensure that tasks align with the overall goal and contextual information 10.
• Context Management: Managing complex and layered context information, including the overall task, specific agent tasks, and shared knowledge, is crucial to ensure alignment with the general objective 10. Aligning the overall task context, context between agents, and context for decomposed tasks within a single agent becomes an intricate challenge 10.
• Tool Interface Design: Agents require well-crafted interfaces to effectively tap into external tools and services, which act as their "arms and legs" 11. This involves providing guardrails for appropriate usage while preventing misuse, such as creating tools for specific queries instead of direct database access 11.
• Integration Complexity: Building secure and scalable tool integrations is challenging 11. The complexity of managing interactions grows proportionally with the number of tools, necessitating sophisticated authentication and authorization layers 11.
• Reproducibility: The non-deterministic nature of agent outputs, influenced by factors like LLM sampling and temperature settings, makes debugging difficult as identical inputs can yield different results 12.

Limitations Related to Robustness, Error Handling, Computational Efficiency, and Scalability

• Robustness: LLM agents are notoriously unpredictable; small perturbations in input can lead to wildly divergent outputs 11. This "prompt brittleness" requires rigorous testing and careful prompt engineering, but eliminating unpredictable behavior remains a significant challenge 11. Cascading error propagation is a major issue, where a small error in one step can spread through shared memory, triggering reactive mistakes and derailing the entire workflow 12. Agents often trust peer messages by default, allowing errors to spread quickly 12.
• Error Handling: Gracefully handling failures, retries, and edge cases across a vast pluggable toolset introduces significant complexity 11. Tool invocation failures, such as calling non-existent functions, mixing up parameters, or returning broken JSON, are common breakdowns 12. Without robust error handling, these issues can quickly lead to system failures 12.
• Computational Efficiency and Scalability:
  • Resource Consumption: State-of-the-art LLMs are resource-hungry, leading to astronomical compute requirements for training and serving 11. Inference costs can quickly balloon with increasing concurrent requests 11.
  • Tool Invocation Overhead: Poorly defined tool contracts and missing guardrails often force agents to improvise interfaces, leading to a messy tangle of calls that are hard to debug at scale 12. Coordination problems can also lead to agents overwhelming rate-limited APIs 12.
  • Latency Bottlenecks: Agents competing for shared resources (GPU cycles, external APIs, databases) cause queues, latency spikes, and timeouts 12. API rate limits, token quotas, and compute starvation are common pain points 12. Hidden synchronization costs between agents can also accumulate, creating latency spikes 12.
  • Memory Management: Synchronizing context and historical data across multiple agents without delays or inconsistencies is difficult 9. Context drift, where agents lose track of important details, can lead to decisions based on incomplete or incorrect information 9. Effective memory management requires sophisticated mechanisms for sharing, integrating, and managing information 10.

Concerns Regarding Tool Hallucination, Generalizability, and Safety

• Tool Hallucination: Agents have a tendency to "hallucinate" knowledge or misinterpret prompts, potentially leading to incorrect or non-existent tool calls 11. This can derail an entire workflow and makes deployments harrowing for mission-critical tasks 11.
• Generalizability: While LLMs excel in specific domains, generalizability across diverse real-world tasks is challenging 13. Achieving multi-step reasoning without human supervision and improving robustness in chained task execution are key challenges 13.
• Safety and Alignment:
  • Goal Misalignment: Increasing agent capabilities raise concerns about them pursuing goals misaligned with human values 11. Constitutional AI techniques aim to bake in behavioral guardrails, but their reliability for constraining superintelligent systems is still uncertain 11.
  • Security Vulnerabilities: The open-ended nature of LLM agents and their potential for misuse raise significant security questions 11. Agents can be coaxed into divulging sensitive information or executing dangerous actions if improperly constrained 11. Prompt injection, where malicious actors manipulate prompts to bypass safety measures or extract sensitive information, is a major concern 11.

Ethical Considerations and Potential Biases

The deployment of multi-step tool invocation planning systems also brings forth critical ethical considerations and potential biases:

• Bias Propagation: If the training data for LLMs or the feedback mechanisms used (e.g., reinforcement learning from human feedback) contain biases, these can be propagated and amplified in the agent's reasoning and tool invocation processes. This can lead to biased decisions or actions, particularly in sensitive applications.
• Control and Transparency: The "black box" nature of LLM agents makes it difficult to understand why a particular decision was made 11. This lack of observability makes auditing for ethical issues and biases challenging. Real-time, fine-grained visibility into agent state remains an open problem 11.
• Accountability: When multi-step tool invocation processes lead to undesirable outcomes, attributing responsibility becomes complex, especially with emergent behaviors where unpredictable system-level patterns arise from agent interactions 12.

Open Research Questions

To overcome the aforementioned challenges, several key open research questions and areas for future development have been identified:

Applications and Use Cases

Moving beyond the foundational understanding of multi-step tool invocation planning and its inherent complexities, its true value is most evident in its diverse practical applications and the significant enhancements it brings to problem-solving across numerous domains. Multi-step tool invocation is critical for artificial intelligence (AI) models, especially Large Language Models (LLMs), as it enables them to execute complex tasks beyond their pre-trained knowledge base 14. This capability transforms LLMs from passive assistants into proactive digital agents, capable of multi-step problem-solving and real-time decision-making 14.

Multi-step tool invocation planning enhances problem-solving in several key ways:

• Enabling Complex Workflows: Models can decompose tasks into smaller, interconnected parts, where the output of one function feeds into the next, facilitating intricate operations like planning, information gathering, and chaining actions .
• Handling Dynamic and Realistic Interactions: Multi-step capabilities allow models to process inputs over multiple dialogue rounds, ask clarifying questions, and adapt their approach based on previous interactions, which is crucial for real-world scenarios with ambiguous or incomplete user input .
• Accessing Real-time Information and External Capabilities: By integrating with external tools, APIs, and databases, LLMs can retrieve live data (e.g., stock prices, weather), execute code, and query specific systems, thereby overcoming the static nature of their training data 14.
• Improving Robustness and Adaptability: This planning, particularly when combined with mechanisms like "Pre-Act," allows agents to incrementally refine their plan after each step's execution, incorporating observations and adapting dynamically if a previous step deviates or fails 3. It also supports identifying and rectifying errors over multiple exchanges 15.
• Context Management and State Awareness: Effective multi-step chains maintain state across steps by passing relevant context and utilizing memory mechanisms, such as conversation history, to ensure continuity and ground LLMs in the task 16. This prevents models from misunderstanding the current state before taking actions 15.
• Augmenting Reasoning: Techniques like "knowledge-augmented planning" inject libraries of proven tool sequences into the planner prompt, improving plan completeness and reducing hallucinations 17. Additionally, "Chain-of-Thought" and "Self-Reflection" approaches enable domain-tuned reasoning and critical evaluation of generated answers 17.

Multi-step tool invocation planning is widely applied across various sectors. The following table summarizes its real-world applications and the specific use cases within them:

These applications span across a broad range of domains, showcasing the versatility and critical nature of multi-step tool invocation planning. Key domains of application include:

• Complex Problem-Solving
• Robotics and Industrial Automation
• Data Analysis
• Software Development and IT Operations
• Healthcare
• Legal Services 17
• Finance and Banking
• Telecommunications 3
• Manufacturing 3
• Logistics 17
• Education 17
• Customer Service (through advanced conversational agents) 3
• E-commerce and Travel (booking systems) 15
• Marketing 17

The continuous advancement of multi-step tool invocation planning is instrumental in transforming LLMs into autonomous agents capable of navigating unpredictable real-world scenarios and accomplishing sophisticated, goal-oriented tasks 3.

Latest Developments, Trends, and Research Progress

Multi-step tool invocation planning remains a cornerstone for enhancing Large Language Models (LLMs), transforming them into hybrid AI systems capable of orchestrating external computations and knowledge access to tackle complex, knowledge- or computation-intensive tasks 18. The core principles revolve around reading, internalizing, and invoking external software tools as callable modules, encompassing planning, retrieval, and calling 18. Recent advancements (2023-2025) have significantly propelled this field forward, directly addressing previous limitations and opening new research avenues.

Novel Algorithms, Improved Architectures, and Integration with Advanced LLM Capabilities

The period from 2023 to 2025 has seen substantial innovation in the intelligence and reliability of multi-step tool invocation planning, focusing on enhancing LLM decision-making, robustness, and efficiency:

• Modular and Iterative Designs: Modern approaches, exemplified by ChatCoT (Chen et al., 2023) and MathSensei (Das et al., 2024), adopt stepwise or iterative reasoning, integrating tool invocation with natural language inference, often within sequential or multi-agent dialogues 18. These modular frameworks enable the composition of external search/retrieval, code generation/execution, and symbolic calculation in cascades 18.
• Decision-Aware and Cost-Sensitive Invocation: Newer methodologies equip LLMs with awareness of their knowledge boundaries and the confidence-cost trade-offs inherent in tool invocation 18. This includes Decision-Search to determine tool necessity and Decision-Call to select the optimal tool based on specific metrics 18. Multi-objective alignment frameworks train LLMs to maximize utility while penalizing unnecessary tool usage 18.
• Graph-Based and Dependency-Aware Planning: Systems like GTool (Chen et al., 2025) explicitly model tool dependencies using request-specific dependency graphs, leveraging graph neural networks for improved tool selection and sequencing 18.
• Reinforcement Learning for Planning: An emerging trend in 2025 is the application of Reinforcement Learning (RL) to tool-integrated reasoning and task planning, with notable works including START, ToolRL, ReTool, OTC, and AutoTIR 19.
• Pipeline Architectures and Modular Fine-Tuning: Frameworks such as Sum2Act (Liu et al., 2024) and ProTIP (Anantha et al., 2023) decompose tool use into explicit stages: planning, action, state-tracking, and reflection, thereby improving efficiency and reliability 20. Plan-based and modular Supervised Fine-Tuning (SFT) aims to disentangle planning from execution to alleviate bottlenecks 20.
• Tool Selection Strategies: Significant progress has been made in how LLMs select tools:
  • Retriever-based methods: Utilize techniques like Sentence-BERT (2019) and Approximate Nearest Neighbor Contrastive Learning (2021) 19. Recent research includes CRAFT (2024), ProTIP (2023), ToolRerank (2024), and Graph RAG-Tool Fusion (2025) 19.
  • LLM-based methods: Involve LLMs in directly determining tool relevance, with advancements seen in ToolLLM (2024), AnyTool (2024), TOOLVERIFIER (2024), and ToolGen (2025) 19.
• Tool Calling and Response Generation: Both tuning-free methods (e.g., RestGPT (2023), EASYTOOL (2024)) and tuning-based methods (e.g., Gorilla (2024), ToolACE (2025)) are under development for robust tool invocation 19. Response generation focuses on either direct insertion or sophisticated information integration from tool outputs 19.

Integration with Advanced LLM Capabilities (Self-Correction, Reflection)

A pivotal area of development has been the integration of advanced LLM capabilities to mitigate common failure modes, particularly through improved reflection and error handling:

• Reflection Learning: Training on "Error → Reflection → Correction" data has demonstrably boosted error correction rates 18. Tool-MVR (Ma et al., 2025), a novel tool-augmented LLM, employs an Exploration-based Reflection Learning (EXPLORE) paradigm, achieving a 58.9% error correction rate on RefineToolBench, a significant improvement over ToolLLM's 9.1% 21.
• Error-Driven Learning and Meta-Verification: Incorporating failed explorations, stepwise preference data, explicit error feedback trajectories, and meta-verification enhances robustness and generalization 20. Multi-Agent Meta-Verification (MAMV) systematically validates APIs, queries, and reasoning trajectories to construct high-quality instruction datasets like ToolBench-V 21.
• Self-Verification: Contrastive question-asking helps resolve subtle distinctions between tools or parameters, thereby improving generalization to unseen APIs 20.
• Addressing Error Modes: Comprehensive analysis reveals prevalent failure types including "No API Call," "API Hallucination," "Invalid/missing parameters," and "incorrect call format" 18. LLMs also exhibit parameter errors, logical errors, and redundant actions, with open-source LLMs showing more parameter and logical errors, while closed-source ones often display redundant actions 22.

New Benchmarks and Innovative Tool Integration Strategies

The rapid evolution of multi-step tool invocation planning is underscored by a proliferation of comprehensive benchmarks and novel integration strategies designed to evaluate and enhance tool-augmented LLMs:

• Comprehensive Benchmarks:
  • API-Bank (2023): Features 73 real-world APIs and 314 tool-use dialogues, using multi-agent data generation to reduce annotation costs 18.
  • ToolBench (2023-2025): A large-scale benchmark comprising 3,451 tools and 16,464 real-world APIs 20. It automates instruction and solution path annotation using LLMs and DFSDT (Depth-First Search in a Decision Tree) 20. Subsequent iterations, StableToolBench (Guo et al., 2024) and RefineToolBench (Ma et al., 2025), introduced virtual API servers and error-focused cycles for stable evaluation and reflection capabilities . ToolBench-V and ToolBench-R (Ma et al., 2025) are new high-quality instruction and reflection datasets addressing prior quality issues 21.
  • SciToolBench (Ma et al., 2024): Specifically evaluates tool-based scientific reasoning across five domains with 856 questions and over 2,400 functions 18.
  • PaperArena (Wang et al., 2025): A novel benchmark for evaluating tool-augmented agentic reasoning on scientific literature, demanding multi-step reasoning, multimodal understanding, cross-document integration, and database interfacing 22. It also includes PaperArena-Hub, an extensible evaluation platform 22.
  • Other Notable Benchmarks: Various other benchmarks address specific aspects of tool invocation:

• Innovative Integration Strategies:
  • Mixture Sampling: Utilized for robust generalization, particularly with unseen tools 18.
  • Alignment Learning: An iterative tuning process for retrieval query generation to optimize retrieval metrics for both in-domain and out-of-domain APIs 18.
  • Autonomous Learning and Rationale Generation: Involves self-critique and Chain-of-Thought explanations to foster causally robust tool learning 18.
  • Token and Embedding Alignment: Addresses the challenge of integrating external tool tokens ("toolkens") by initializing embeddings via pooling and L2 regularization 18.
  • Virtual API Server Infrastructure: Crucial for next-generation evaluation sets, providing simulated, reproducible APIs and stable automated assessment protocols, thereby enhancing evaluation rigor 20.

Key Research Questions Currently Being Pursued

Current research actively seeks to address persistent challenges and expand the capabilities of multi-step tool invocation planning:

• Improving Planning and Invocation Reliability: Efforts are concentrated on overcoming unreliable tool planning and invocation, which often stems from low-quality instruction datasets and hallucinated API calls 21. Systematic verification of APIs, queries, and reasoning trajectories is a primary focus 21.
• Enhancing Tool Reflection and Error Correction: A critical area is addressing the weak tool reflection abilities of current models, where a high percentage of errors remain uncorrected 21. This involves developing learning paradigms that leverage explicit "Error → Reflection → Correction" cycles based on tool feedback 21.
• Generalization to Unseen Tools and Scenarios: Developing robust strategies for generalization is crucial, as LLMs often tend to overfit to known tools 18.
• Optimizing Tool Selection and Usage Efficiency: Researchers aim to reduce inefficient tool usage, including invoking more tools than necessary or showing biases towards general-purpose tools 22. The goal is precise and context-aware tool selection 20.
• Complex Problem-Solving in Specific Domains: Enhancing agents' abilities in multi-step, multimodal, and cross-document reasoning is vital, especially in knowledge-intensive fields such as scientific literature analysis 22.
• Robust and Scalable Evaluation: Developing more rigorous and comprehensive evaluation methodologies is essential to assess planning, retrieval, and execution capabilities, moving beyond mere outcome accuracy to include tool selection precision, sequence planning, and error correction rates . Addressing evaluation instability with real-world APIs through virtual simulation remains a key challenge 20.
• Multi-Agent Coordination: Exploring how multi-agent systems can improve accuracy and tool-use efficiency through better coordination and division of reasoning labor for complex tasks is an active research area 22.

Speculative Advancements and Potential Future Trajectories

Based on current trajectories, several significant advancements and future directions for multi-step tool invocation planning are envisioned:

• Autonomous Learning and Adaptation: Future agents are expected to feature more advanced autonomous learning capabilities, including sophisticated self-critique and the generation of causally robust tool learning rationales 18. Continual tool integration will also be paramount 20.
• Deeper Error Model Integration: The integration of more sophisticated error models will lead to more robust error recovery mechanisms that extend beyond current reflection techniques 20.
• Higher-Fidelity Evaluation: There is an expectation of higher-fidelity human judgment benchmarks to complement automated evaluations, alongside refined simulation environments like MirrorAPI for more robust testing 20.
• Multi-Modal and Real-World Interaction: Future research will increasingly prioritize richer, multi-modal scenarios and the handling of more realistic user instruction noise to improve tool interaction in complex environments 20.
• Resource-Aware Planning: Agents will need to be designed with larger token budgets and more efficient parallelization strategies to effectively manage complex tasks while controlling computational costs 22.
• Heterogeneous Agent Systems: The development of heterogeneous systems that strategically leverage the distinct strengths of specialized LLMs for appropriate sub-tasks, rather than relying solely on a single, generalist model, is a promising direction 22.
• Enhanced Memory and Self-Correction: Incorporating explicit memory modules to record reasoning chains, guide subsequent actions, and prevent repetition will be crucial 22. Further enhancing self-correction and robust error handling will be critical for task success and efficiency 22.
• Unified and Safe Tool Learning Frameworks: The field is moving towards unified frameworks that ensure safe, robust, and accessible tool learning, addressing challenges such as high latency and the need for comprehensive tools 19.