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Rescue
Users wait 8 seconds for a response. Tool calls stack sequentially when they could run in parallel. A simple query calls GPT-5 when GPT-5 mini would answer it just as well at 10× lower latency. Your RAG system embeds the query, searches, reranks, and assembles context in series when three of those steps could overlap.
We profile your agent or RAG pipeline, identify the latency bottleneck, and implement targeted fixes. In most cases, a 50–70% latency reduction is achievable without any change to the quality of the output.
Tell us what broke.
Most slow agents have one or two primary bottlenecks. Profiling tells us which. These are the five we see most often.
GPT-5 averages 1.2–2.5 seconds time-to-first-token. GPT-5 mini averages 0.3–0.6 seconds. Claude Haiku averages 0.4–0.8 seconds. If you are routing every query (including simple factual lookups) to a frontier model, you are paying 3–5× the latency on calls that don't need it. The fix is model routing: classify the query complexity and route simple queries to a smaller, faster model. In most systems we profile, 40–60% of queries are simple enough that GPT-5 mini or Haiku handles them without quality loss. That alone reduces median latency by 50% or more.
An agent that runs tool_A, waits for the result, then runs tool_B, waits for the result, then runs tool_C takes three times as long as an agent that runs all three in parallel. Most LangChain and LangGraph pipelines serialize tool calls by default. They were built for correctness, not speed. If the three tools are independent (which they often are), running them in parallel with Promise.all in TypeScript or asyncio.gather in Python cuts the combined tool latency by 60–70%. The fix requires identifying which tool calls have no data dependency on each other and restructuring the pipeline accordingly.
Embedding the query, running a vector search, and reranking results in series adds 200–800ms to every request. Some of that is unavoidable, but much of it is not. If your system embeds the same five queries repeatedly (popular questions, boilerplate queries, system-level checks), caching those embeddings eliminates most of that cost. Moving to a faster embedding model for the query path (ADA-002 is not the only option) can save 100–200ms per query. Skipping reranking for high-confidence retrievals (when the top result scores well above the second result) saves the reranker latency on the easy cases.
Passing 10,000 tokens of retrieved context when 2,000 would answer the question forces the LLM to process more tokens on every call. That increases cost and increases time-to-first-token roughly linearly with context length. If your top-k retrieval returns 20 chunks and the answer is almost always in the top 3, you are paying the latency cost of processing 17 useless chunks on every request. Fix: tighter retrieval with a lower top-k, a chunking strategy review to make sure each chunk is dense enough to be worth retrieving, and a reranker that surfaces the right chunks so you can truncate the rest.
If your agent runs on a serverless function (AWS Lambda, Vercel Functions, Google Cloud Functions), the first request in a session pays a 2–4 second cold start penalty while the function initializes. Users experience this as a randomly slow response, sometimes fast, sometimes painfully slow. The fix depends on your architecture: warm function instances via scheduled pings, moving latency-sensitive paths to edge workers with faster cold start times, or moving persistent compute for workloads where function startup latency is unacceptable.
We measure before we fix and measure again after. Every claim about improvement is backed by numbers, not observations.
We instrument each step in your pipeline and measure actual p50, p95, and p99 latency. Not estimated latency. Not what the API docs say. What your system actually takes on your actual queries against your actual infrastructure.
Written report identifying which steps account for more than 50% of total latency and why. We rank fixes by expected impact so you know where to start.
We implement the highest-impact fixes from the root-cause report. Model routing, parallelization, caching, streaming, or infrastructure changes, depending on what the profiling shows.
p50, p95, and p99 latency before and after, measured on the same query set under the same conditions. Numbers you can show to your team and your users.
Some latency problems don't have an engineering solution at the application layer.
Agents with fundamentally slow external dependencies
If one of your agent's tools calls a third-party API that takes 5 seconds to respond and you can't change that API, we can't fix that. We can parallelize other calls to minimize the wall-clock impact, but we can't make a slow API fast.
Teams who need sub-200ms total latency for a multi-step agent
A single LLM API call to a frontier model costs 300–600ms at minimum. A multi-step agent that makes three calls is physically bounded at roughly 900ms even in the best case. Sub-200ms for a full multi-step agent is not achievable with current LLM APIs. If that is your requirement, the architecture needs to change, not the optimization.
Agents that need a full architectural rewrite to fix latency
Some systems are slow because of architectural decisions that can't be fixed with configuration changes, a synchronous chain that was never designed for parallelism, or a data model that forces unnecessary roundtrips. If our profiling shows the fix requires a rewrite, we'll tell you that clearly and quote the rebuild separately.