Want to scale AI agents without breaking anything? Retrieval engineering is the answer.

On September 24, GigaOm’s Whit Walters and Vespa.ai’s Bonnie Chase will explain what breaks when hundreds of AI agents hit retrieval at once.

Sep 3rd, 2026 11:38am by Alex Wilhelm

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A September 24 webinar curated by The New Stack will examine why fragmented retrieval stacks can buckle when hundreds of AI agents query company data at once. (Image: Allison Saeng for Unsplash+)

AI agents are multiplying as corporations adopt the technology in record numbers. Smarter underlying models, better tool use, and improved multi-agent collaboration have pushed agents to evolve beyond impressive demos into practical technology that companies marshal in production environments. But the job’s not finished.

As companies deploy more agents, more often, and against longer tasks, the plumbing that provides their AI ephemera with the required information is buckling.

Here’s the problem: AI agents are sending waves of queries against company data, creating concurrency issues and exposing just how difficult it can be to ensure a company’s AI-legible information is fresh, served only when relevant, and quickly available.

Join the live conversation: On September 24 at 12 p.m. Eastern/9 a.m. Pacific, Whit Walters, Field CTO and Lead Analyst at GigaOm and author of Defeating the Integration Tax report, joins Bonnie Chase, Director of Product Marketing at Vespa.ai, to discuss what happens when retrieval architecture meets that workload.

And crucially, they will explore in this live conversation what changes when a team rebuilds it as a unified layer instead of a fragmented one.

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You might be asking yourself: How has this problem not been solved yet? Google famously handles tens of thousands of search queries every second; how difficult can it be to serve agents the information that they need when we’ve solved the human version of the same problem? It’s no small challenge, and it’s why retrieval engineering is a labor category you’ll hear more about in coming quarters.

So, why is the problem worse with AI? Agents don’t ask a single question. They may retrieve data, reason against it, and then go back for more context. That doesn’t sound too complicated, until we recall that companies often stitch multiple systems together to provide their agents with required information. In practice, that means fusing vector databases, ranking tools, and serving layers into a single hybrid retrieval system that serves ever more agentic queries.

Worse, when several agents ping the same cobbled-together architecture at once, relevance drift becomes a real issue. You might do all the work to get your company or team up and running with agents, only to see the effort fail because of stale data, generic answers, or even truncated results as retrieval plumbing stumbles.

Your AI agents can’t scale successfully if they get dumber the more agents you deploy. So join the conversation on September 24, where we’ll break down how you can solve your retrieval engineering woes.

What you’ll take away:

Why agent workloads create a fundamentally different retrieval challenge than added concurrency alone
The specific failure modes at agent scale — latency stacking, stale context, relevance drift
Why fragmented retrieval stacks amplify those failures
What a unified retrieval architecture looks like in practice

Alex Wilhelm is a journalist focused on technology and finance. He co-hosts the This Week in Startups podcast, and writes the Cautious Optimism newsletter. He was previously Editor in Chief of TechCrunch+.