The Last Bottleneck

Thirty years ago, software shipped on shrink-wrapped discs and was sold on retail shelves. Getting a product to a customer meant manufacturing, packaging, and distribution deals. All of those processes were complex, so the path from builder to buyer was full of obstacles.

Every major shift in product development since then has removed a bottleneck. The internet removed distribution. Cloud and continuous deployment removed the friction of iteration, taking teams from yearly releases to shipping daily. Today, AI tools like Lovable and Cursor are further removing technical complexity. They removed coding language constraints so more people could prototype and build faster, regardless of their technical expertise. A single team can ship a new feature in hours. And they do. Constantly.

But none of these shifts solved the most important question: are you building the right thing?

Building speed has outpaced learning speed

Today, teams will ship a new feature before lunch and move on to the next build by dinner. A campaign can go live in the morning with no read on performance until a monthly metrics report. Understanding how users actually experience what you built still requires connecting data systems, waiting on analyst availability, and hoping to answer the right question before the next sprint starts.

Speed without understanding doesn’t just waste a single sprint or campaign; it multiplies the cost of every wrong decision. Every feature built on a flawed assumption ships faster now. Every campaign optimized against the wrong metric scales faster. Every experiment launched without clear insight into user behavior could be pushing customers away faster than it brings them in. Before teams prioritize shipping speed, they need a workflow to determine if they’re moving in the right direction.

Teams aren't just shipping faster, they're shipping blind faster. Without a way to connect the dots between what you ship and what’s working, speed doesn't inherently create value, it just stalls growth.

From reporting to reasoning

This product evaluation bottleneck isn't caused by a lack of data. Teams have more data than ever. And it isn't caused by a lack of tools. The analytics market is enormous. The problem is that most analytics processes still operate the way they did before AI changed everything else. Someone has a question. They file a ticket with an analyst. Days or weeks later, they get a dashboard, but it’s only helpful if they asked the right question in the first place.

That workflow was acceptable when teams shipped quarterly. It's unworkable when they ship daily. In a world where products, campaigns, and experiments move at the speed of AI, analytics has to proactively keep up. It has to work continuously, reasoning about what's happening in real time to surface what matters before anyone thinks to look for it. Reporting on the past is a recipe for failure. Analytics tools today need to actively help teams understand the present and decide what to do next.

That evolution requires a fundamentally different foundation.

AI-scale analytics requires full, behavioral context

Determining whether you've built the right thing takes more than a data warehouse and a query layer. It requires a complete picture of the user experience, including behavioral data and funnels, session replays, survey feedback, and support conversations. You need to blend real-time data about how your users behave, how they feel, and where they get stuck.

But user signals are only half of the picture. AI also needs to understand context about your team:

• What questions are being asked right now?
• Which charts and metrics were recently created?
• How has your taxonomy evolved?
• How are priorities shifting as products and campaigns change?

Without this living context, even powerful AI produces shallow or misleading answers. This is why you can't just put an LLM on top of a data warehouse and expect meaningful insight. That might give you a faster way to ask what happened, but you’re missing the most important parts: diagnosing why and determining what to do next. Real understanding requires blending the full context of your user experience and the intent of the team's building for those users.

Analytics must become agentic—not just intelligent

Today, the operational burden of analytics still falls on people. Someone has to maintain the taxonomy as the product changes. Someone else has to clean up outdated or broken data so the system doesn't quietly degrade. Another person has to connect behavioral patterns and user feedback to concrete ideas about what to fix, test, or build next. All of this work is essential, and almost none of it scales.

Agentic analytics is the solution to that work. It maintains and evolves the foundation it depends on, keeps context fresh without being asked, and helps teams move from insight to action without waiting for a handoff. Analytics stops being a passive system you query and becomes an active participant in how you build, ship, and grow.

Upgrading the Amplitude AI Analytics Platform

Cursor redefined what's possible in software development, making engineers up to 10x faster and making building accessible to people who never wrote a line of code. Today is that moment for analytics.

We are adding Amplitude Agents to our AI Analytics Platform: an agentic system that opens the door for more people and more teams to understand behavioral data and the full context of user experiences.

It continuously senses what’s happening across the customer journey. It decides what matters and why. It acts through experiments, personalization, and more. With AI Analytics, teams will move from building products to evaluating their impact in minutes, not weeks. That’s a 10x productivity gain over manual analytics.

Here’s how it works.

Amplitude Global Agent

The Amplitude Global Agent has access to analyze and take action across the full Amplitude platform: behavioral data, journeys, experiments, session replays, and feedback. It also incorporates the context of what your team is focused on, including recent questions, new charts, shifting metrics, and updated priorities. It doesn’t only build charts to answer questions; it has conversations in plain language, encouraging follow-ups to guide anyone through analytical inquiries.

Global Agent runs full investigations, connecting patterns across data sources and preparing concrete next steps. Root cause analysis that used to take days or weeks now takes minutes, with higher accuracy than manual investigation. After all that digging, it can even make recommendations about the right steps to take next. And it can be accessed where your teams spend most of their time—in Slack and Teams.

Specialized Agents

While Global Agent helps teams investigate and decide, Specialized Agents make sure nothing important is missed in the first place. They are purpose-built for a single domain or workflow, running on a scheduled basis to analyze what's happening and push summaries, opportunities, and recommended actions to your Amplitude agents page, email, or Slack.

Currently, there are four Specialized Agents available:

• Dashboard monitoring agent: Auto-generates insights from any dashboard, deep dives into interesting trends and anomalies, and runs proactive monitoring on a schedule so issues surface before they become problems.
• Session replay agent: Reviews hundreds of sessions on a scheduled basis to identify friction and patterns costing conversions. Recommends specific fixes with quantified impact.
• Website conversion agent: Proposes experiments, generates variants, and creates experiment configurations. With your permission, it will execute launches and share results along with rollout decisions.
• Feedback agent: Generates scheduled summaries of top themes from unstructured feedback (e.g., surveys, tickets) and surfaces actionable insights. teams would otherwise miss.

Model Context Protocol (MCP) Server

With MCP, Amplitude's behavioral intelligence is available directly inside the AI workflows you already use. You can ask questions, investigate issues, and act on what you find without leaving your workflow. Insight shows up at the moment of decision, not after the fact.

AI Assistants: Ask Claude or ChatGPT to summarize user behavior, pull charts, and answer product or campaign questions using Amplitude data.

Development: Validate, debug, and track impact with product context inside Cursor or Claude Code. Add behavioral context to GitHub pull requests. Build and ship AI features with integrated product insights in AWS Kiro.

Design & Prototyping: Validate and refine Lovable concepts with product performance and feedback. Identify user friction and generate data-backed prototypes in Figma Make.

Product & Collaboration: Analyze and surface product opportunities with Notion agents. Make Amplitude charts and analytics searchable in Atlassian Rovo.

Sales & Engagement: Personalize messaging and targeting in Outreach with behavioral signals.

One system, not separate features

Global Agent, Specialized Agents, and MCP aren't three separate products. They're one system built on a shared foundation of behavioral data, living context, and agentic reasoning. The Global Agent investigates in real time. Specialized Agents automate workflows that matter to you. MCP puts it all where your team already works. Together, they close the loop between building and learning that the rest of the industry has left open.

The future belongs to teams that learn as fast as they ship

Building speed is no longer a competitive edge. AI has given every team the ability to ship extraordinarily fast. Instead, what matters is how fast you can understand what to build and turn that knowledge into consistent action.

Analytics that operates at human speed will fall behind. The future is AI-scale analytics; grounded in behavioral context, continuously reasoning, acting on insights, and embedded where teams already work.

This is the future Amplitude is building. And it's available today.

Go behind the scenes and see it in action on Thursday, March 5th. You’ll get a tour of the platform from the people who know it best: the builders who made it. Register today.