Latency Budget Design for AI Agents — How to Control the Trade-off Between Thinking Time and Response Time

A latency budget is a methodology for designing and allocating the maximum allowable response time for an AI agent to complete a single task execution. In agents that perform multi-step reasoning, latency accumulates as the number of LLM (Large Language Model) calls increases, and miscalibrating the balance between thinking time and final response time can lead to degraded user experience and system failures.

This article is intended for engineers and technical leads responsible for agent design, and systematically covers the following topics.

When someone says "the response is slow," where does the cause actually lie? For a single chatbot, suspecting the model's inference speed is usually sufficient. But with AI agents, it's not that simple.

Before returning a single response, an agent runs multi-step reasoning, calls external tools, and has an orchestrator consolidate the results. Because each of these processes stacks sequentially, the latency perceived by the user tends to diverge significantly from the speed of the model alone.

Designing a latency budget means understanding this cumulative structure and deliberately deciding how much time to allocate to each component. Attempting to optimize by simply trying to "make everything faster" without accounting for the structure often just shifts the bottleneck elsewhere. The starting point for design is to decompose and examine the latency structure of your own agent.

For a single LLM call, the main latency factors come down to two points: Time to First Token (TTFT) and the number of output tokens. By understanding the time from when a request is sent until the first token is returned, along with the subsequent token generation speed (TPOT), you can grasp nearly the full picture of latency.

In contrast, multi-step reasoning agents have a significantly different structure. The latency components can be broken down as follows:

• TTFT accumulates for each LLM call — if there are 5 steps, TTFT occurs 5 times
• Tool execution and external API call wait times are interspersed at each step
• Because the output of the previous step becomes the input of the next, latency cascades through sequential dependencies

It's tempting at first to think, "Even if I add more reasoning steps, the per-step latency is small, so it's fine." In practice, however, the TTFT and tool wait times at each step stack sequentially, so total latency grows rapidly as the number of steps increases. With a 5-step agent averaging 2 seconds per step, that alone adds up to over 10 seconds.

With this distinction in mind, designing a latency budget requires fundamentally different approaches for single calls versus multi-step workflows. For single calls, the primary levers are model size reduction and quantization-based speedups, whereas for multi-step workflows, the priority is time allocation per step and reducing the number of steps itself.

Chain-of-Thought (CoT) and test-time compute scaling (Test-time Compute) are powerful techniques for improving LLM answer accuracy, but they come at the cost of a significant increase in output token count.

The main effects of CoT on latency are as follows:

• Increased output tokens: Because intermediate reasoning steps are generated sequentially, token counts can be several times to tens of times higher compared to returning only the final answer
• Cumulative TPOT: As the formula TPOT(ms) = (request_latency - TTFT) / (total_output_tokens - 1) shows, overall latency grows linearly as the number of output tokens increases
• Internal steps in reasoning models: Reasoning models such as the o1 series internally generate large numbers of "thinking tokens," causing latency to accumulate in ways that are not visible from the outside

For test-time compute scaling, the appropriate judgment depends on the nature of the task. In cases where accuracy is the top priority—such as mathematical proofs or complex code generation—increasing computation steps is rational; however, in cases where speed is paramount—such as FAQ responses or routine form-filling assistance—it is more appropriate to omit or minimize CoT.

As practical guidelines, refer to the following.

"Why does an agent that only calls five tools take more than 30 seconds?"—many engineers working in the field have encountered this question.

Unlike a single LLM call, latency in agent orchestration has a structure where multiple processing steps are chained sequentially, causing delays to accumulate additively. The main sources of latency can be organized as follows:

• LLM inference latency: The sum of TTFT (Time to First Token) and TPOT (Time Per Output Token) incurred at each step
• Tool call latency: Round-trip time to external APIs, databases, and search engines
• Orchestrator decision latency: Re-querying the LLM to determine the next step
• Context carry-over cost: Prompt length that grows each time the results of the previous step are appended to the context window

The problem is not that these exist independently, but that they form a sequential structure in which the output of each step depends on the input from the previous one. If a process that takes 3 seconds per step runs for 10 steps, a minimum of 30 seconds of latency will occur. Furthermore, as the context grows larger, TTFT in later steps tends to increase as well.

Identifying steps that can be parallelized and designing a task graph accordingly is the first countermeasure against this accumulating structure.

When implementing a multi-step reasoning agent, you encounter a fundamental contradiction. Increasing reasoning steps improves answer quality. But it also slows down responses proportionally.

The problem is that how much of this latency is acceptable varies enormously depending on the type of task and the context of use. In a real-time conversational interface, users who wait more than three seconds will abandon the interaction, whereas for a data analysis agent running in the background, a 30-second processing time may be perfectly acceptable. In other words, the decision to "improve accuracy" can directly trigger user abandonment or system failures. If this tradeoff is not consciously managed at the design stage, it becomes impossible to course-correct later.

Applying CoT (Chain-of-Thought) to reasoning models tends to improve answer accuracy. However, the tradeoff is an increase in output tokens, causing latency to grow linearly. This is the essence of the "accuracy-speed dilemma."

At first, it is tempting to think: "Since adding thinking steps improves quality, we should always use multi-step reasoning." In practice, however, applying deep reasoning regardless of task complexity has been reported to introduce delays of several seconds to over ten seconds even for simple queries, degrading the user experience. The benefits of improved accuracy are limited to tasks that genuinely require complex reasoning.

The main factors that give rise to this dilemma are as follows:

• Token generation cost: The more reasoning steps there are, the greater the cumulative Time Per Output Token (TPOT), causing overall Request Latency to balloon.
• Chaining of intermediate steps: In CoT, intermediate outputs are fed as input into the next prompt, causing the context window to grow and making TTFT (Time to First Token) prone to degradation as well.
• Non-linearity of inference-time scaling: Even as Test-time Compute increases, accuracy gains diminish while latency continues to rise.

As an approach to resolving this dilemma, a "routing strategy" that dynamically switches models and reasoning depth based on task complexity is effective.

The situations that demand both a good user experience and high task quality vary greatly depending on the use case of the AI agent.

For example, in a customer support chatbot, users are waiting in front of their screens, so response speed directly shapes the experience—yet incorrect answers risk damaging the brand, making quality equally non-negotiable. For a code generation or review agent, developers can tolerate some waiting, but output containing bugs increases the cost of downstream processes. Document summarization and report generation can be executed asynchronously as background tasks, allowing more thinking time to be allocated with quality as the priority.

The common decision criterion across all of these is a single question: is the user waiting? It is rational to design for speed in situations where users are waiting for a real-time response, and to design for quality in situations where results are received asynchronously.

Another factor worth considering is the cost of task failure. In many cases, returning accurate information a few seconds later causes less business loss than returning incorrect information immediately. Conversely, in situations like FAQ responses where minor errors are tolerable, the risk of prioritizing speed is limited.

If this tradeoff is not made explicit at the design stage before implementation proceeds, you will end up receiving the contradictory feedback of "too slow" and "too low quality" simultaneously. Defining acceptable latency and quality standards for each use case in advance is the starting point for sound design.

Have you ever wondered why design principles differ so dramatically between real-time and asynchronous systems? The reason is that the nature of the tradeoff itself is different, which means applying the same resource allocation logic to both tends to cause the design to break down.

Real-time systems are those in which users expect to receive a response immediately after providing input—AI chatbots and voice assistants are prime examples. In these cases, the longer the thinking time, the higher the risk of user abandonment, and increasing CoT (Chain-of-Thought) steps to pursue accuracy directly degrades the user experience by the same measure. The upper bound on acceptable latency is often required to be within a few seconds, making minimization of TTFT (Time to First Token) the top-priority metric.

Asynchronous systems, on the other hand, have a structure in which the system itself absorbs the wait time until processing is complete. Examples include overnight batch document summarization and background report generation agents; mechanisms such as OpenAI's Background mode, which can retain generated response data for approximately 10 minutes, are well-suited for this purpose. Since throughput and cost take priority over latency, it is easier to justify increasing reasoning steps to improve accuracy, and timeout settings can be configured more coarsely with a more relaxed throttling design.

What matters most is the case where the same agent switches between real-time and asynchronous modes of operation.

Conclusion: Because the optimal solution for latency reduction techniques differs depending on the objective, cost, and accuracy requirements, it is necessary to understand the characteristics of each technique and design their combination accordingly.

We compare representative techniques across three axes: inference acceleration, model lightweighting, and retrieval latency management.

When looking to accelerate inference, it is tempting to first consider modifying the model's architecture or weights. In practice, however, many cases have been reported where combining two approaches—decoding strategies and quantization—can reduce latency without significantly compromising accuracy.

Speculative Decoding is a mechanism in which a small "draft model" reads ahead by generating multiple tokens, which a larger model then verifies in a single batch.

• The draft model rapidly generates n tokens → the larger model determines acceptance or rejection in parallel
• The higher the acceptance rate, the fewer calls to the larger model, reducing TPOT (Time Per Output Token)
• This approach tends to be most effective when the vocabulary distribution of the task is predictable (e.g., templated responses, code completion)

Quantization compresses model weights from FP32 to INT8 or INT4, reducing GPU memory bandwidth consumption and improving inference throughput.

• Inference servers such as NVIDIA Triton recommend benchmarking against both Time to First Token (TTFT) and Inter-Token Latency metrics
• Even if quantization improves TTFT, always verify on an evaluation set that accuracy degradation remains within acceptable bounds
• Approaches that combine fine-tuning with quantization, such as QLoRA, are also worth considering

From a latency budget design perspective, which metric is treated as the bottleneck is a critical question.

Using large-scale models for every inference step tends to be inefficient from a latency budget standpoint. Selecting the appropriate model based on the nature of the task leads to a better balance between speed and accuracy.

The criteria for delegating processing are determined by task complexity and response speed requirements.

• Cases suited to SLMs (Small Language Models): Pre- and post-processing steps with clear patterns, such as intent classification, keyword extraction, and conversion to fixed formats. Suitable when response speed is the priority and accuracy requirements are limited.
• Cases suited to large-scale LLMs: Complex multi-step reasoning, interpretation of ambiguous natural language, and judgments requiring specialized knowledge. Used when accuracy is the top priority and some degree of latency is acceptable.

A two-tiered approach—delegating simple branching decisions or short-text classification to an SLM, and escalating to an LLM when complex reasoning is required—is effective for conserving latency budget.

MoE (Mixture of Experts) models embody a design philosophy that achieves this kind of model selection within a single model. Because they switch between specialized Expert sub-networks activated according to the input tokens, not all parameters need to be used on every call. As a result, they tend to reduce inference costs compared to a Dense Model of equivalent accuracy.

The following summarizes key implementation considerations.

Anyone who has operated RAG in a production environment may be familiar with the experience of "slow retrieval causing a bottleneck across the entire agent's response."

In standard RAG, a sequence of steps occurs: the user's question is embedded, a vector database is searched, and the retrieved chunks are passed to the LLM. Because this retrieval step happens only once, its impact on latency is relatively limited.

Agentic RAG, on the other hand, is a different matter. Because additional retrieval occurs at each step of multi-step reasoning, retrieval latency accumulates within the loop. The primary sources of latency are as follows:

• Embedding generation: The process of vectorizing the query occurs at every step
• Nearest-neighbor search in the vector database: Search time increases as index size grows
• Hybrid search integration processing: Additional computational cost arises when combining semantic search and BM25 results using methods such as RRF

Practical techniques for managing retrieval latency are as follows:

• Leveraging prefix caching: Absorb repeated references to the same document through caching.

Conclusion: Latency budget design begins with time allocation across the task graph, and is operated through a three-tiered approach of throttling and monitoring.

Having understood the concepts, the next step is to move on to the actual design and allocation process. The three pillars of implementation are: time allocation based on task complexity, timeout policy configuration, and real-time monitoring via AI Observability.

When designing a latency budget, it is tempting to assume that time should simply be distributed evenly across all steps. In practice, however, it is more effective to visualize the dependencies and importance of each step using a task graph, and to assign a different budget to each step accordingly.

A task graph is a representation of the sequence of processes executed by an agent as a directed acyclic graph (DAG). Each node corresponds to a processing step such as a "tool call," "LLM inference," or "RAG retrieval," and edges represent dependencies.

The basic procedure for time allocation is as follows:

• Identifying the critical path: Identify the longest dependency chain in the graph and concentrate the majority of the budget there
• Step classification: Separate synchronous steps where the user is waiting from asynchronous steps that can be executed in the background, and prioritize budget allocation for the former
• Quantifying the budget: For example, if the total budget is set at 10 seconds, explicitly assign an upper limit to each node—such as 2 seconds for intent interpretation (LLM inference), 1.5 seconds for RAG retrieval, 4 seconds for tool execution, and 2.5 seconds for response generation
• Reserving buffers: Add a buffer of approximately 10–15% to each step to absorb network round-trips and variability in external APIs

By defining the task graph, it becomes possible to pinpoint which step is causing latency using AI Observability tools.

Throttling and timeouts function as "safety valves" for the latency budget. After allocating time in the task graph, without a mechanism to suppress or cut off processes that are likely to exceed the budget at runtime, the design values become nothing more than theoretical ideals.

Throttling Configuration Policy

Throttling is a technique that controls the number of concurrent requests and token generation rate with upper limits. The decision criteria for configuration are as follows:

• For user-facing interfaces that require real-time responses, strictly limit the maximum number of output tokens per request and prioritize response speed
• For batch processing and asynchronous tasks, prioritize maximizing throughput and set a more lenient upper limit on the number of concurrent executions

Note that overly strict throttling can degrade quality on complex tasks.

Hierarchical Design of Timeout Policies

Rather than setting a single timeout value, it is important to layer timeouts according to the granularity of processing.

• Step timeout: A short timeout set for individual LLM calls or external tool calls (e.g., a few seconds to tens of seconds)
• Agent task timeout: The upper time limit for an entire task. Allow more margin than the sum of step timeouts
• Session timeout: The time limit for an entire multi-turn conversation

The behavior upon timeout firing must also be defined in advance.

"We've set a latency budget, but can we actually grasp in real time how much time each step is consuming in the production environment?"——Continuing operations without being able to answer this question means that identifying the cause of budget overruns will always be reactive.

AI Observability is a system that centrally visualizes measurements across each processing phase of an agent—including LLM inference steps, tool calls, and external API integrations. Unlike conventional application monitoring, it is important to track the following metrics individually:

• TTFT (Time to First Token): The time until the model returns the first output token
• TPOT (Time Per Output Token): The output latency between tokens. Calculated by the formula (request_latency - TTFT) / (total_output_tokens - 1)
• Step-by-step cumulative latency: The total time spent on each node in the task graph

The key implementation point is to embed measurement into the agent's orchestration layer and assign a trace ID at the step level. This allows you to immediately identify which tool calls or LLM hops are straining the budget.

For alert design, a configuration that issues a warning when 80% of the budget is consumed and automatically triggers fallback processing (switching to a simplified response or returning a cached response) before reaching 100% is effective.

When designing with a single agent, the conversation ends at "this model is slow." But once multiple agents begin to collaborate, latency chains together, and before you know it, the overall response time becomes a simple accumulation of each agent's individual delays.

To control latency in a multi-agent system, a design is needed that distributes the budget not as "how much for the entire system" but as "how much for each agent." What becomes particularly impactful here is the latency of A2A (Agent-to-Agent) communication and the choice of execution method—whether to run agents in parallel or sequentially. With sequential execution, each agent's latency is simply added together, whereas with parallel execution, the slowest agent becomes the bottleneck. Which is more advantageous depends on the task's dependency relationships and cannot be determined uniformly.

Every time communication occurs between agents, three stages accumulate: serialization, network round-trip, and deserialization. This latency, invisible in a single-agent setup, tends to become apparent in multi-agent configurations using the A2A (Agent-to-Agent Protocol).

At first, it is easy to think that "the more finely you divide agents, the easier parallelization becomes," but in practice, when the granularity of division is too fine, cases have been reported where communication overhead exceeds inference costs and overall latency worsens.

Main Factors by Which A2A Communication Amplifies Latency:

• Payload size bloat: Passing the entire context window between agents causes a sharp increase in transfer volume
• Synchronous waiting: When sequential dependencies—where downstream agents wait for upstream completion—chain together, latency accumulates multiplicatively
• Re-authentication and token validation: In configurations where OIDC token validation runs on every call, round-trip latency is further added

Effective Countermeasures:

• Context compression and summary passing: Pass only summarized intermediate results to adjacent agents rather than the full history
• Leveraging prefix caching: Caching common instruction sections can significantly reduce TTFT (Time to First Token)

Use parallel execution when there are no dependencies between tasks, and sequential execution when the output of a previous step becomes the input of the next—clarifying this decision criterion from the outset helps prevent misallocation of the latency budget.

Cases Where Parallel Execution Is Effective

• Search and retrieval phases that simultaneously call multiple external APIs
• Processing that summarizes independent document chunks in parallel
• Tasks where different tools (computation, code execution, data retrieval) can be launched simultaneously

In these cases, the total completion time of all tasks converges to a single bottleneck, enabling a significant reduction in overall latency.

Cases Where Sequential Execution Is Necessary

• Multi-step reasoning where the next judgment is made based on the inference result of the previous step
• Processing that proceeds by incorporating tool call results into a CoT (Chain of Thought)
• Workflows where error handling and conditional branching chain together

In sequential execution, latency accumulates in proportion to the number of steps, making timeout settings for each step essential.

Practical Guidelines for the Choice

An effective approach is to draw a task graph to visualize dependency edges, then classify groups of independent nodes together into parallel batches. By clearly separating the parts that can be parallelized from those that require sequential execution, the latency budget can be consumed most efficiently.

Note that parallel execution simultaneously consumes thread and coroutine resources, so as the number of concurrently executing agents increases, GPU and network bandwidth can become bottlenecks. For an overview of multi-agent system design as a whole, see [What is Multi-Agent AI?

Conclusion: Understanding failure patterns that tend to be overlooked during the design phase and taking preventive measures is directly linked to the stable operation of latency budgets.

The two main failure factors are "unbounded resource consumption" and "Context Window bloat." The detection methods and mitigation strategies for each are explained in turn below.

Trying to fill a bathtub with the faucet left wide open means water keeps overflowing with no way of knowing when it will stop. Unbounded Consumption is exactly that same situation. When an agent repeats loops or retry logic without termination conditions, the latency budget is exhausted in no time.

Typical overrun patterns are as follows:

• Missing loop termination conditions: Infinite retries trigger on tool call failures, consuming several times the budgeted tokens and time
• Chained sub-agent invocations: An orchestrator sequentially launches multiple sub-agents, and the delays from each step accumulate
• Waiting on external API timeouts: Waiting for responses from external services becomes a blocking operation, causing the overall latency to stall for extended periods

Real-time measurement using AI observability tools is essential for detection. Specifically, the following metrics should be monitored:

With Context Window bloat, it is tempting to think that passing more information improves accuracy, but in practice the more common problem is that it triggers a cascading degradation of latency.

The main pathways through which bloat causes delays are as follows:

• Increased TTFT (Time to First Token): As the number of input tokens grows, the time the model spends on attention calculations increases at a rate that is linear or worse
• Reduced prefix cache hit rate: Passing a different long history each time prevents the cache from being reused, making it impossible to achieve the over 96% TTFT reduction demonstrated in Vertex real-world measurements
• Propagation to the orchestration layer: In multi-step reasoning, the output of step one is appended to the input of the next step, so inputs grow larger in later steps and delays accumulate

Three effective approaches for mitigation are as follows:

• Context compression: Replace past conversation history with summary tokens and remove unnecessary details
• Sliding window: Retain only the most recent N turns and periodically discard older history
• Context isolation per Task Graph unit: Pass only the information required for each subtask to prevent shared context from bloating

Particular caution is warranted in cases where an agent decides "just to be safe" and accumulates all tool call results.

Conclusion: Latency budget design is a structural decision-making process for simultaneously pursuing both "speed" and "intelligence," and multi-layered management tailored to task complexity, execution mode, and system configuration is indispensable.

The content covered in this article is organized into the following key points.

Understanding the delay structure is the starting point Distinguishing between TTFT (time until the first token is produced) and TPOT (time per output token) and visualizing which step is causing delays is the first step in design. In multi-step reasoning, delays from each step accumulate, making time allocation using a task graph an effective approach.

Switch strategies based on execution mode The basic policy is to use streaming and delegation to an SLM (Small Language Model) in real-time scenarios to ensure perceived speed for users, while combining background execution with Test-time Compute in asynchronous scenarios to prioritize accuracy.

Combine reduction techniques Speculative Decoding, quantization, prefix caching, and MoE (Mixture of Experts) each apply to different situations. Rather than relying on a single technique, combining them according to the bottleneck maximizes their effectiveness.