Sierra AI、Pinecone Nexus で独自ベンチマーク首位

Let's clear something up first. Sierra AI, Bret Taylor's AI company, recently announced they had “AI-pilled” the whole company with an internal agent they built and named Pinecone. Great name!

Here is the part where the joke turns serious. Today, on τ-Knowledge, the benchmark Sierra itself built, an agent using Pinecone Nexus posted the top score, ahead of agents running on frontier models alone. Two companies reached the same conclusion independently: in enterprise production, it is the knowledge, not the model, that decides whether AI works.

We did not arrive here overnight. We have been building Nexus for about a year. More than 800 organizations signed up for early access, and since Early Access opened in May we have worked closely with over a hundred enterprises, many of them among the largest in the world, running their Data through it. Today Nexus is generally available. This is what we learned, and why the thesis held.

Start with what those enterprises told us, because it is probably familiar. They moved agents out of the demo and into production, across finance, insurance, legal, retail, and support. The agents stalled. Not because the model was not smart enough. Because of everything the agent had to do before it could be smart.

Picture a support agent handling a billing dispute. Before it can answer, it reads the ticket, searches the knowledge base, reads what came back, searches again, and re-sends everything it has gathered on the next turn. Most of its time and most of its token budget is spent before it decides anything. Then the retrieval itself betrays it. A vector search returns the top matching chunks of text, stripped of the relationships that connect them, and the agent confidently quotes a refund policy that was revised eighteen months ago. A fluent answer grounded in the wrong version of the policy scores zero, and in production it reaches a real customer.

Now multiply that by every ticket, every day. The bill does not scale with the price of a token. It scales with retrieval. Blended inference prices fell about 67% year over year, and enterprise AI budgets kept climbing anyway, because one task fans out into dozens of model calls, each re-reading the same documents. More than 85% of an agent's effort goes to fetching knowledge before it reasons. Goldman Sachs projects token consumption to multiply 24x by 2030. In a survey of 306 teams running agents in production, reliability, not model capability, was the top challenge, and 68% cap their agents at ten steps before a human has to step in. A bigger model does not fix a bill that scales with retrieval, or a completion rate capped by what the agent can reach.

There is a second cost. The model is a commodity, because every competitor can buy the same one. The only durable advantage an enterprise has is its own knowledge and the way its people work. Most agent stacks reassemble that knowledge on every call and hand it to a model vendor. Satya Nadella has made this the center of Microsoft's argument: models are becoming interchangeable, and the moat that lasts is the enterprise's own data, context, and memory, kept under its own control. Alex Karp of Palantir has put it more sharply, warning that frontier labs have oversold their models while quietly absorbing the proprietary edge of the companies paying for them, so enterprises end up paying to lose their advantage. Different companies, same warning from two leaders serving world’s largest enterprises. The moat leaks out one API call at a time.

That is the problem Nexus was built to remove, and the fix is not a better model. It is moving the knowledge work out of the per-query loop.

Nexus compiles your data once, ahead of time, into governed, domain-specific knowledge, and agents reuse that compiled layer on every call. The person who understands the work describes it in their own terms: the entities that matter, how they relate, and the shape of the answers the work needs. Nexus turns raw sources into structured knowledge, including the relationships that ordinary retrieval throws away, and resolves conflicts between sources up front, so the layer knows what it knows and flags what is contested. Agents then ask through KnowQL, a query language built for agents. The agent states what it needs and gets back a typed, cited answer in a single call. Compile once, answer every time.

Go back to the support agent. With a compiled layer it stops grinding through documents and asks for the answer instead. It gets the current policy, with a citation, and because it now has customer-level context it knows the one question only the customer can answer, and when to ask it. We know this because we pointed Nexus at our own support queue on July 17. The share of tickets the agent resolved on its own went from 24.6% to 55.1%. More than half of our tickets now close without a person touching them.

The same shift shows up on Sierra's benchmark. τ-Knowledge grades an agent on whether it drives the system to the correct end state, and its hardest domains make the agent find and apply the right policy before it acts. On the banking domain, ninety-seven of those tasks, we gave the same frontier models a Nexus layer to query and watched their behavior change.

The model calls roughly halved, and each one carried less context. GPT-5.2 gained 12% accuracy at 80% lower cost. GPT-5.5 held its accuracy at 77% lower cost. In dollars, that is a task that cost $1.45 falling to $0.53, and the advantage held on 96 or 97 of the 97 tasks, so it is not an average hiding a wide spread. Across the full benchmark, an agent with Nexus posted the top score, 47.4% against the best frontier model's 46.4%, at 74% less cost per task.

Task completion is the number benchmarks chase. Cost is the number that decides whether an enterprise AI program returns anything. When the same model delivers the same accuracy at a third of the prices, AI finally delivers on the promised enterprise ROI business case. It also drops low enough that, in many cases, smaller and open-weight models clear the bar, which compounds the saving. The AI strategy initiative now becomes the Production AI initiative.

None of that matters in a regulated industry if the answer cannot be defended, which brings us back to Nadella and Karp. Both are describing a crisis of trust: keep control of your knowledge, and be able to prove where a decision came from. Nexus is built that way. It runs in your own cloud, on the models you choose, with no standing Pinecone access to your data, so your knowledge never leaves your infrastructure. Every field it returns carries a citation and a confidence score. Every answer traces back to the source document and clause it came from. Access control is applied when knowledge is retrieved, not requested in a prompt. That is what lets an agent's answer survive a security review and an auditor, and it is what turns moat preservation from a keynote line into something an enterprise can operate.

One more thing the hundred-plus enterprises taught us. They validated accuracy fast, usually in the first week. The rest of the work was keeping the layer true as the world moved: new tickets daily, contracts amended, a process doc revised on a Tuesday, and the wiki that disagrees with the contract. Across those engagements they compiled 3.5 million source chunks into nearly 26,000 structured knowledge artifacts, drawn from support tickets, contracts, filings, research papers, and call transcripts. Nexus curates incrementally, so only what changes gets recompiled, and the person who owns the domain keeps control of how the knowledge is shaped. The layer worth building is the one still true in month twelve, not just the one that demos well in week one.

You do not wait for a better model to build a reliable agent. You give the model better knowledge.

Pinecone Nexus is generally available today, in your own cloud. Learn more at pinecone.io/nexus.