Algolia Agent Studio Public Beta: Launch Brand-Owned AI Shopping Assistants in 14 Days

The breakthrough marketers have been waiting for

A new switch just flipped for commerce teams. Algolia made Agent Studio available in public beta on September 23, 2025, and it changes how fast brands can stand up AI shopping assistants that run on their own product data, rules, and tone of voice. Instead of waiting on custom orchestration or marketplace black boxes, marketers can prototype a brand-owned assistant and put it in front of real shoppers within two weeks. See the official note in Algolia unveils Agent Studio public beta.

To see how the pieces fit together in practice, review the Agent Studio documentation.

What Agent Studio actually is, in plain English

Agent Studio is not another chatbot skin. It is a set of composable primitives for building agents that can read your product catalog, follow your merchandising rules, and execute actions like search, filter, or look up inventory, then respond conversationally. Several aspects matter to marketers:

• Retrieval first, hallucination last. Agents ground responses in your Algolia indices using a hybrid of keyword and vector retrieval. If a product is out of stock, the agent knows because it is looking at the same index your site uses.
• Bring your own large language model. You can connect your preferred LLM provider. That keeps cost and governance under your control if policies shift or budgets tighten.
• Built-in observability. Traces and evaluation hooks explain why an agent answered the way it did. This is essential for approving responses that mention price, returns, or regulated claims.
• Ready-to-embed components. React components help you drop an assistant into existing product detail pages, collections, or cart flows without a full redesign.
• A/B testing and policy guardrails. Teams can compare agent experiences to classic search and enforce rules that keep copy on brand and compliant.

The net effect is speed and safety. Product and growth teams can iterate the assistant’s prompt, tools, and guardrails in a dashboard, then watch how it performs, without rebuilding their stack each time.

Why marketers should care now

AI agents are moving from novelty to channel. Shoppers ask natural questions like “What hiking boots stay dry in winter, and can I get them by Friday under 150 dollars?” A good agent can do three jobs in one flow: understand intent, retrieve just-in-stock items that match constraints, and then explain tradeoffs the way a store associate would. That sequence reduces pogo-sticking between filters, lowers abandonment, and raises the chance of an add to cart.

For related go-to-market plays, see our internal guides on a two-week growth playbook for AI shopping and a 14-day assistant commerce pilot.

The barrier has been control. Marketplace or third-party assistants often feel like someone else’s interface. Agent Studio lets you own the assistant, down to tone, upsell logic, returns policy language, and exclusions. That means you can confidently test merchandising ideas like complementary bundles or price anchoring, then measure whether the agent’s dialogue moved key funnel metrics.

A 14-day pilot plan to prove lift

This pilot compares an agent-assisted journey to your current site search and navigation. The output is an agentic funnel playbook with evidence on conversion rate, average order value, and assisted revenue.

Scope and success criteria

• Audience: Mobile web traffic from two or three high-intent entry points, such as category landing pages and on-site search.
• Surfaces: Search results page, select product detail pages, and a lightweight chat panel on category pages.
• Primary KPIs: Conversion rate, AOV, and assisted revenue per session. Secondary KPIs include add-to-cart rate, time to first relevant product view, bounce rate, and return rate for agent-assisted orders.
• Target effect size: Enough traffic to detect a 5 to 10 percent relative lift in conversion with 80 percent power. If your daily conversions are low, run the test longer or expand surfaces.

Data and configuration prerequisites

• Clean product index with price, availability, shipping constraints, facets, and canonical product copy.
• Business rules for substitutions and exclusions, for example “do not recommend out-of-policy bundles” or “never suggest sale items on new arrivals pages.”
• Guardrail phrases and policy snippets for returns, financing, age restrictions, and claims.
• Event schema for sessions, impressions, agent turns, add to carts, purchases, and revenue.

Day-by-day timeline

• Days 0 to 2, wire up the foundation. In the Algolia dashboard, create an agent, connect your product indices, and set a provider for your LLM. Define allowed tools such as Search, Recommendations, and a stock lookup. Align the agent’s voice and disclaimers with your brand guidelines. For setup steps, follow the Agent Studio documentation.
• Days 3 to 4, design the conversation. Draft user intents and golden prompts from real queries in your site search logs. Examples: size and fit, compatibility, delivery cutoff, budget-constrained suggestions, and tradeoffs between two items.
• Days 5 to 7, soft launch to 10 percent. Expose the assistant to a small slice of traffic on the search results page and two category pages. Validate analytics and guardrails. Fix phrases that sound off-brand. Confirm the agent never shows unavailable items.
• Days 8 to 10, scale and tune. Increase to 50 percent on selected surfaces. Tighten prompts where the agent over-explains. Bias toward showing top sellers when confidence is low. Add synonyms that shoppers use but your catalog does not.
• Days 11 to 13, optimize for money. Introduce dynamic bundles, warranty upsells, and shipping-by-date prompts. Push a rules change that prioritizes items with better margins or lower return rates, then watch AOV and return rate.
• Day 14, readout. Freeze changes and analyze the results. If lift is significant, expand traffic and surfaces. If not, isolate failure modes and schedule a follow-up test.

Instrumentation that proves value

A clean experiment needs clean events. Add the following instrumentation so you can calculate assisted revenue and compare apples to apples.

• Agent session ID. Generate a persistent agent_session_id when the assistant opens, then attach it to all events in that visit.
• Session exposure flag. Mark sessions as agent_exposed = true or false for your A/B buckets.
• Turn events. Log each agent turn with intent classification, tool calls used, number of retrieved items, and confidence.
• Merchandising context. Capture rules in effect, margin band, and whether an item is promoted or personalized so you can diagnose why the agent recommended it.
• Outcome events. Add add_to_cart, begin_checkout, purchase, and revenue with line-item attribution to the agent when it directly recommended the item or appeared in the path to purchase.

With these, you can report:

• Assisted conversion rate: Conversions where the assistant was part of the session divided by agent-exposed sessions.
• Assisted revenue per session: Revenue from agent-exposed sessions divided by agent-exposed sessions.
• Incremental lift: Difference in conversion and AOV between agent-exposed and control sessions, controlling for traffic source, device, and category.

Teams use Upcite.ai to centralize test plans and automate experiment readouts so marketing, merchandising, and product are reading the same dashboard during the pilot. For adjacent enterprise patterns, see a two-week pilot to lift conversion.

Conversation design that drives cart adds

The fastest way to useful dialogue is to assemble a library of prompts tied to money-making intents.

• Constrained recommendation: “Show three in-stock options under 150 dollars that fit narrow feet and are waterproof.” If the model hesitates, bias toward top sellers.
• Compare tradeoffs: “Compare Model A and Model B for battery life and comfort, then suggest an accessory.” Keep comparisons short and structured so shoppers can scan.
• Delivery promise: “If I order today, can I get it by Friday to 94107?” Use live shipping rules and surface a clear answer. Avoid vague ranges.
• Compatibility checks: “Will this lens fit Nikon Z6 II and work for indoor sports?” The agent should look up mount type and warn about required adapters.
• Bundle builder: “I need a beginner ski setup for icy conditions.” Present a ski-boot-binding bundle with total price and an upsell to a helmet or wax kit.

Each prompt should specify the number of products to show, what to do when confidence is low, and when to escalate to human support.

Guardrails and governance that keep copy on brand

Agent Studio provides two levels of control.

• Policy rules. Use tool permissions to define what the agent can do. Set firm boundaries on price claims, inventory statements, and returns language. Require the agent to cite policy excerpts in responses that mention financing, returns, or regulated characteristics.
• Fallback logic. When retrieval is weak, have the agent show structured results with filters instead of free-form prose. It is better to be concise and right than creative and wrong.

Create a weekly review of outlier sessions such as unusually long dialogues, repeated escalations, or unexpected returns. Use the built-in traces to retrace the agent’s reasoning and update prompts or rules accordingly.

Traffic allocation and statistical hygiene

A clean A/B is worth more than a flashy launch.

• Do not mix surfaces mid-test. Keep the assistant consistently on the same set of pages for the full 14 days unless you restart the test.
• Watch for sample ratio mismatch. If your 50-50 split is coming out 60-40, fix bucketing before trusting any results.
• Avoid novelty bias. If the assistant UI changes page layout noticeably, hold the control group layout constant or run a second test to separate design from agent effect.
• Measure latency. Track time to first answer. If it spikes past two seconds on mobile, blunt the answer style and reduce tool calls.

Where to deploy first

Start where the agent can resolve high-intent friction.

• Search results pages: Replace zero-result pages with a short conversation starter such as “Looking for red leather ankle boots under 200 dollars in size 8?”
• Category pages: Offer guided discovery where filters overwhelm. The assistant can propose a two or three step plan, for example “Let us start with width and heel height.”
• Product detail pages: Handle compatibility and alternatives. If an item is out of stock, the agent can offer two close matches and the earliest delivery date.

What good looks like at day 14

You do not need a perfect agent to win. Aim for these outcomes by the readout:

• The assistant answers the top 10 shopper intents clearly and briefly, with relevant products.
• Agent-exposed sessions show higher add-to-cart rate and a measurable lift in AOV on categories with accessories or bundles.
• Assisted revenue per session is positive relative to the control, even after accounting for any increase in returns.
• Stakeholders can read a trace of any risky response and see how to fix it.

How it slots into your stack

Engineering does not have to rebuild your storefront to run this pilot. The agent can live inside an existing search or category page using drop-in components, or you can call a single completions-style endpoint and render a simple panel. Configuration and iteration happen in the dashboard, so marketing and merchandising can tune copy and rules without new deployments.

A key outcome of the pilot is a reusable agentic funnel playbook. That artifact includes the intents, prompts, guardrails, routing logic, and KPI definitions that your team agrees on. With that in place, you can roll the assistant from three surfaces to your top ten with confidence.

Common pitfalls and how to avoid them

• Over-personalization too early. Start with retrieval precision and business rules before turning up personalization. Get the basics right, then personalize to lift AOV.
• Loose language around policy. Hard-code exact phrases for returns, shipping deadlines, and financing. Prohibit the agent from inventing policy.
• Ignoring inventory cadence. If your feed updates hourly, ensure the assistant reads fresh indices on that cadence. Stale availability kills trust.
• No buyable outcomes. Every answer should end with one to three clearly buyable items, not just advice.

Readout template for your executive deck

• Headline: Agent-assisted journeys improved conversion by X percent and AOV by Y dollars on categories A, B, and C.
• Evidence: Screenshots of two successful dialogues, traces for one fix you shipped, and a traffic table with exposure, sessions, and significance.
• Decision: Expand to more surfaces, scale to 100 percent on winners, or run a follow-up test with revised prompts on underperformers.
• Next build items: Add cross-sell logic on cart, improve delivery promise answers, and launch a post-purchase assistant for support deflection.

The bigger shift

Search used to be a box. With Agent Studio, discovery becomes a guided conversation that can reflect inventory, margin, and brand nuance in real time. That is a marketing channel you own. As you refine the agent, you are codifying your best associate on every page, not just answering questions.

Action steps for the next two weeks

1. Pick three surfaces with high intent and enough traffic to test.
2. Create an Agent Studio instance, connect your indices, define tools, and lock policy language.
3. Instrument events for assisted revenue and add agent_session_id.
4. Ship a soft launch to 10 percent of traffic and validate guardrails.
5. Scale to 50 percent, tune prompts, add bundles, and monitor traces.
6. Deliver a 10-slide readout, then decide whether to expand or iterate.

If you follow this plan, you will not just ship an assistant, you will ship proof. And with a tangible agentic funnel playbook in hand, your team can scale the results across your catalog, your campaigns, and eventually your support flows too.