Speeding Up AI Agents: A New Approach to Faster Inference

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Author: Denis Avetisyan

Researchers are exploring techniques to dramatically accelerate the decision-making process of language-based AI agents by predicting and pre-calculating potential actions.

This paper details optimizations for agentic language model inference using speculative tool calls, asynchronous execution, and caching strategies to maximize throughput.

While language models increasingly rely on external tools to enhance reasoning and interact with their environments, these interactions often create performance bottlenecks during inference. This paper, ‘Optimizing Agentic Language Model Inference via Speculative Tool Calls’, introduces a series of optimizations-including speculative tool call prediction and caching strategies-designed to accelerate agentic LM workflows. Our approach achieves substantial throughput improvements by minimizing overheads through both client-side and engine-side techniques. Could these optimizations represent a crucial step toward deploying truly responsive and scalable language-agent systems?

The Inevitable Bottleneck of Sequential Thought

The proliferation of smart devices and digitally-mediated services is driving a surge in demand for intelligent agents capable of navigating increasingly complex interactions. These agents are no longer simply tasked with responding to direct commands; instead, modern applications require them to proactively assist users with multi-step tasks, synthesize information from diverse sources, and adapt to dynamic environments. Consider applications ranging from personalized healthcare, where agents might coordinate appointments, monitor vital signs, and adjust medication dosages, to sophisticated financial planning, where they analyze market trends and offer tailored investment strategies. This necessitates a shift from reactive systems to proactive entities capable of anticipating user needs and autonomously orchestrating solutions-a challenge that pushes the boundaries of current artificial intelligence capabilities.

Despite remarkable advancements in natural language processing, current language models often encounter difficulties when tasked with actions requiring external tools or multi-step reasoning. The fundamental bottleneck isn’t a lack of knowledge, but rather the sequential nature of their operation; each step – formulating a plan, selecting a tool, executing that tool, and then interpreting the result – must be completed before the next can begin. This serial processing introduces significant latency, particularly when dealing with tools that themselves have response times, or when complex problems necessitate numerous iterative steps. Consequently, even highly capable models can appear sluggish or unresponsive in dynamic environments demanding real-time interaction, hindering their practical application in scenarios like robotics, automated decision-making, and interactive assistance. The challenge, therefore, isn’t simply building more powerful models, but fundamentally rethinking how they orchestrate and execute complex tasks to overcome this inherent latency.

The fundamental constraint on the performance of many intelligent agents is the reliance on serial processing. This means each computational step – be it formulating a query, accessing a tool, or interpreting a result – must be completed before the next can begin. While seemingly innocuous, this sequential approach creates bottlenecks that severely limit both scalability and responsiveness. As tasks become more complex, requiring numerous interdependent steps, the cumulative latency grows proportionally. Unlike parallel processing, where multiple operations occur simultaneously, serial processing prevents the agent from proactively exploring alternative paths or anticipating future needs. Consequently, the agent appears sluggish and struggles to handle dynamic environments or real-time demands, hindering its ability to effectively interact with the world and ultimately limiting its practical applications.

Anticipating the Next Token: A Glimmer of Parallelism

Speculative decoding operates by employing a smaller language model to generate draft tokens in advance of the primary, larger model. This precomputation occurs in parallel with the primary model’s processing, allowing for a potential reduction in overall latency. The smaller model predicts several future tokens, which are then either accepted by the larger model if deemed correct, or corrected if discrepancies are identified. This parallelization is effective because the primary model does not need to generate each token sequentially; it can verify or revise precomputed drafts, effectively shifting the computational burden and enabling faster response times.

Speculative decoding reduces latency by employing a smaller language model, such as xLAM, to generate draft tokens in parallel with the primary, larger language model. The primary model then functions as a verifier, accepting correct predictions and correcting any inaccuracies in the draft. This parallel processing allows for a significant reduction in overall token generation time, as the primary model avoids processing every token from scratch; it only evaluates and potentially modifies the pre-computed draft. The efficiency of this approach is directly tied to the predictive accuracy of the smaller model and the speed of the verification and correction process.

Testing with the xLAM-2-8B model demonstrated an 80% accuracy rate in speculative prediction of subsequent tokens. This metric represents the proportion of predicted tokens that directly matched the tokens generated by the primary language model without requiring correction. While not perfect, this level of accuracy allows for substantial parallel precomputation, as the majority of predictions are valid and can be directly incorporated into the final output, minimizing the computational load on the primary model and reducing overall latency. The 20% error rate necessitates mechanisms for efficient error detection and correction, which are crucial for maintaining output quality.

Efficient implementation of speculative decoding requires infrastructure capable of managing both the initially predicted tokens and any subsequent corrections generated by the primary language model. This involves optimized data pipelines for parallel processing and minimal overhead during verification. Furthermore, the overall system performance is directly impacted by prediction accuracy; lower accuracy necessitates more frequent corrections, increasing computational load and potentially negating latency benefits. Therefore, careful evaluation and tuning of the draft model, alongside robust error handling mechanisms, are crucial for maximizing the effectiveness of this approach.

Internalizing the Oracle: Engine-Side Speculation

Engine-side speculative tool calling centralizes both the prediction of tool usage and the verification of those predictions directly within the inference engine itself. This architectural choice is designed to significantly reduce communication overhead typically associated with client-server interactions; instead of repeatedly sending requests to external tools and awaiting responses, the engine proactively predicts tool calls and validates those predictions internally. By keeping these processes contained within a single system, latency is minimized and the overall speed of agent inference is improved, as the engine avoids the network delays inherent in traditional, distributed architectures.

Optimized inference is achieved through the integration of three key components: vLLM, a high-throughput and memory-efficient serving system; a Tool Cache, which stores previously computed tool outputs to avoid redundant calls; and dynamic batch size management. vLLM enables rapid model execution, while the Tool Cache significantly reduces latency by providing instant access to frequently used tool results. Batch size is carefully adjusted to maximize throughput without exceeding memory constraints, ensuring efficient utilization of available resources. These combined optimizations minimize overall inference time and maximize the number of tokens processed per second.

Evaluation using the BFCL dataset indicates that implementing speculative tool calling, in conjunction with vLLM, a Tool Cache, and optimized batch sizes, yields a measurable reduction in agent inference overhead. Specifically, observed performance gains demonstrate time savings of up to 21% per agent turn. This improvement signifies a substantial decrease in the latency associated with each interaction, allowing for faster response times and increased overall efficiency in agent-based applications. The benchmark results validate the efficacy of these combined optimizations in a practical, representative environment.

Optimization of the inference engine via engine-side speculative tool calling resulted in substantial throughput gains, consistently exceeding hundreds of tokens per second. These improvements were measured during testing with the BFCL dataset and are attributable to the combined effects of reduced communication latency, the utilization of a Tool Cache for fast access to previously computed tool outputs, and efficient batch size management within the vLLM serving framework. The observed throughput increases directly translate to a faster response time for agents and a greater capacity for handling concurrent requests.

Engine-side speculative tool calling demonstrates a measurable performance advantage over client-side implementations, yielding an additional 2-3% reduction in agent turn latency. This improvement stems from minimizing network communication associated with tool calls; by performing both prediction and verification of tool usage within the inference engine itself, the need to transmit requests and responses between the client and server is substantially reduced. This localized processing decreases overall request completion time and contributes to a more efficient inference pipeline, particularly noticeable in scenarios with frequent tool interactions.

The efficacy of engine-side speculative tool calling is directly correlated with the accuracy of the speculative model; diminished accuracy necessitates increased validation and correction, potentially negating performance gains. Robust validation mechanisms are therefore critical, involving thorough verification of speculative tool outputs against actual results. These mechanisms must include both automated checks and, potentially, human-in-the-loop oversight to ensure reliability and prevent the propagation of errors. Failure to maintain a high degree of speculative accuracy will lead to increased latency due to frequent re-computation, and could ultimately degrade overall system performance.

A Future Unbound by Sequentiality

Current large language models often face bottlenecks due to the sequential nature of text generation – a process known as serial processing. Researchers have addressed this limitation by integrating speculative decoding with engine-side optimization techniques. Speculative decoding anticipates future tokens, allowing parallel processing of multiple potential continuations, while engine-side optimization refines the underlying computational framework to maximize efficiency. This combined approach effectively bypasses the constraints of generating text one token at a time, dramatically increasing throughput and enabling significantly faster response times. The result is a pathway towards building agents capable of handling complex requests and engaging in more fluid, real-time interactions, representing a substantial leap forward in artificial intelligence responsiveness.

Asynchronous agent design represents a significant departure from traditional serial processing, enabling agents to manage multiple tasks simultaneously and dramatically improve overall responsiveness. By decoupling task initiation from task completion, these agents capitalize on increased computational throughput; rather than waiting for one operation to finish before starting the next, they intelligently interleave processing, maximizing efficiency. This concurrent handling of requests isn’t simply about speed; it allows the agent to maintain a fluid, interactive experience, responding to new inputs even while existing operations are underway. The result is an agent that feels remarkably nimble and capable of handling complex, dynamic interactions in real-time, paving the way for more sophisticated and user-friendly artificial intelligence systems.

Current agentic systems often struggle with the demands of intricate, dynamic environments due to inherent processing bottlenecks. Recent advancements, however, present a robust framework for constructing agents capable of managing these complexities in real-time. By integrating techniques like speculative decoding with optimized engine-side processing, these methods move beyond traditional serial processing limitations, enabling agents to anticipate and respond to multiple facets of an interaction concurrently. This parallel approach not only boosts throughput but also significantly reduces latency, allowing for more fluid and natural interactions. The result is a system poised to underpin the next generation of intelligent agents, promising breakthroughs in applications ranging from highly responsive virtual assistants to truly autonomous robotic systems capable of navigating and reacting to unpredictable situations with unprecedented agility.

The confluence of advancements in agent technology promises a new era for intelligent, responsive systems across diverse applications. Virtual assistants, freed from the constraints of sequential processing, can now manage multiple user requests concurrently, offering a truly interactive and seamless experience. Simultaneously, autonomous systems – ranging from robotics to self-driving vehicles – stand to benefit from quicker reaction times and improved decision-making in dynamic environments. This heightened responsiveness isn’t merely about speed; it’s about creating agents capable of engaging in complex, multi-faceted interactions, adapting to unforeseen circumstances, and ultimately, delivering more intuitive and effective support for human endeavors. The potential extends beyond convenience, offering solutions for time-critical applications where real-time processing is paramount, and paving the way for increasingly sophisticated levels of automation.

The pursuit of accelerated inference, as detailed within, isn’t merely about shaving milliseconds off processing time; it’s about cultivating a responsive system. One anticipates the inevitable branching paths of agentic tool calls, much like predicting the growth of a complex root system. Donald Knuth observed, “Premature optimization is the root of all evil,” and this work echoes that sentiment. While striving for speed, the authors carefully balance optimization with the inherent unpredictability of these agentic systems. The techniques for minimizing overhead – asynchronous execution, prefix caching – aren’t rigid controls, but rather carefully considered supports, allowing the system to grow organically, adapting to the demands placed upon it. It understands that every refactor begins as a prayer and ends in repentance, as it acknowledges the ever-evolving nature of such a system.

What Lies Ahead?

The pursuit of accelerated agentic inference, as demonstrated by speculative tool calls, merely shifts the locus of eventual constraint. Throughput gains are not victories against latency, but temporary respites. The KV cache, so meticulously optimized, will inevitably swell with the ghosts of discarded speculations – a monument to the system’s inability to not explore every branching path. One splits the computation, but not the fundamental problem of combinatorial explosion.

The emphasis on client-side and engine-side coordination implies a belief in manageable complexity. Yet, asynchronous execution, while promising, introduces a new surface for failure – a tangled web of dependencies where a single stalled speculation can trigger cascading delays. Every optimization, every layer of abstraction, is a prophecy of future brittleness. The system grows more sensitive, not more robust.

Future work will undoubtedly focus on more sophisticated speculation strategies, perhaps incorporating reinforcement learning to predict ‘good’ tool calls. This is merely rearranging the deck chairs. The true challenge isn’t maximizing throughput, but accepting the inherent limitations of a system built on prediction – a system destined to be surprised, and to fall, as all interconnected things eventually do.

Original article: https://arxiv.org/pdf/2512.15834.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-12-20 08:17