Stop watching sessions. Let AI agents like ZipyAI debug and prioritize them for you.

In today's world - do you still need to watch millions of session replays or millions of errors that get tracked? 

You still need the data, you still need to collect user actions and you still need to track errors. 

But No - you don’t need to watch and review each and every session or error? You don’t need a person watching these sessions for you and figuring out what’s wrong. 

That’s where Agents are bringing about the change, you need someone doing this job for you. Someone going through the session replays and issues and figuring out what’s wrong. Where someone else is scraping through millions of data points and telling you “ Hey this is important”, “ This is where you are losing revenue” “ This is where most users are dropping off” “ Hey - Fix this - its costing you a lot of users”

Let’s take a case study, an e-commerce application where millions of users are shopping. You just launched some new brands expecting an increase in revenue, but weeks go by and instead of increase, you actually see a drop in revenue.  Business teams to product teams to engineering teams, all wondering why the new launch wasn’t a success. 

Imagine these magical revenue agents (Except they are not magical in reality - they are just smart) - coming in and telling you “Team - While adding new brands and new pages, you actually broke  one of your payment integrations and that’s what is causing you the drop off” - fix it and you will see 10% jump in your revenue next week. 

Isn’t that insane? How is that for an AI Agent? An AI Agent, doing all the investigation on your behalf, constantly watching and reasoning and telling you the root cause and also predicting how it will fix things. 

To answer the previous question, in todays world you don’t need traditional session replay and error monitoring tools, you need something smarter, something that gives more actionable insights on outcomes and problems, rather than throwing millions of data points at you. 

What all tools are currently used for understanding product usage? 

These are usually called product analytics tools. They track user behaviour by logging in user interactions, drop offs, engagements, bounce rates etc. How do they do these things? 

They track every user session data, click events, mouse movements, scrolls, page navigations and much more. Some of these capture in a detailed way, some just do sampling. Some are free like GA4 and some are paid like Heap, Hotter, Mouseflow, Logrocket, Fullstory. 

What all tools are currently used for understanding problems and errors faced by users? 

These are usually called observability tools. These track all your errors across various platforms and libraries like python, javascript, java and they catch all errors occurring in production environments - be it web apps or mobile app. Comprehensive tools like Datadog, sentry do a fantastic job of catching all errors. But is it humanly possible to classify and understand which bugs matter and which don’t. These tools, while great at finding all issues, don’t help you with prioritisation? Who does the prioritization? Well there is a human in this loop - who is constantly getting alerted and is prioritising what matters. What matters - may differ business to business, product to product, but eventually there is one metric that every company is worried about - revenue. Now can there be someone solving for revenue when looking at observability data and also prioritising the same for you? 

Are tools like logrocket, full story, mixpanel, Datadog, sentry - obsolete in today's Agentic world? 

There has to be a shift now for smarter products, more agentic products which now do the job for you instead of you reactively fetching and comprehending data. Let’s break it down into steps:

1. What do these tools do today?

• These tools capture a lot of useful data 
• Data is great, grouping it intelligently, alerting users
• Visualising trends like adoption, breakages

2. What is the job being done by human beings today?

• 1. Study the sessions and error data from product analytics and monitoring tools 
• Understanding where the real issues lie
• Interpreting trends based on human reason 
• Prioritising based on their understanding of business and revenue impact. 
• Suggest Fixes once this is done 
• Development teams will now fix, test , deploy the fix

3. How much time is taken and how many people involved in the above activities?

• Each of the activity will take hours or days depending on the amount of data, where the problems are
• Each activity will take many teams to understand, analyse and fix these.

4. What part of this job can be taken up by agents?

• 1. Agents can scan and understand all sessions and error data fed to them 
• Agents can now reason where the problems lies
• Based on Revenue or User Impact Agents can Prioritise 
• Agents can also propose or fix the issue with human in the loop 
• Agents can also fix, test and deploy the fixes.

5. What happens when Agents replace humans?

• Agents take much less time, maybe minutes to do all activities 
• Humans will be only required to assess and approve the whole process

Ecommerce & SaaS: From Reactive Tools to Agentic Products

🛒 E-commerce Example: Online Fashion Retailer

Most e-commerce teams already know the routine. You have tools like Google Analytics, Hotjar, Mixpanel, Shopify Analytics, and Zendesk. They track everything—50,000 daily sessions, cart abandonment (usually around 68%), funnel dropoffs, tickets, load times. They’ll even group users into neat little segments and flash warnings when abandonment spikes. Dashboards show conversion dropping, mobile checkout breaking, certain product pages bleeding users.

So what happens next? Humans step in. An analyst burns half a day watching session replays of abandoned carts. The UX team discovers users fumbling with a size chart on mobile. Payments time out at peak hours. Marketing notices abandoned carts tie back to bad ad campaigns. A product manager has to play judge: is the mobile bug (40% of traffic) more urgent than better recommendations (20% of revenue)? Designers tweak flows, devs hack in retry logic, QA tests, rollout takes two weeks.

Twelve people, three weeks. That’s the cost of figuring out one problem.

Now imagine an agent doing that job. Not sampling—reading every session in real-time. Not just spotting symptoms, but connecting dots: checkout failures tied to Android devices, one payment method, one region. It doesn’t just point out the problem. It tells you: fix this, you recover $50K a month; fix that, you gain $20K. It even drafts the solution—“add a retry after three seconds,” “make size charts inline on mobile”—and pushes code with rollback ready.

Suddenly the same work takes thirty minutes, not three weeks. The human role shifts: not digging, not guessing, but reviewing what the agent already surfaced, then moving on to bigger bets like new markets or features.

💼 Saas Example: Project Management Platform

SaaS teams live in dashboards. They’ve got Datadog, Sentry, Amplitude, Intercom, PagerDuty. These tools watch millions of API calls, track adoption across tens of thousands of users, flag slow responses, and visualize trends. On the surface, you feel covered. Dashboards say: feature usage is down 25%, support tickets spiking from integrations, queries slowing down.

But the heavy lifting still falls on people. An SRE combs error logs. A product analyst stares at adoption charts. Support leads categorize complaints. Engineers dig until they find a database connection issue. Product realizes collaboration features confuse users. Customer success notices adoption tanked right after a UI change. Meetings pile up. Then comes the decision: fix database slowness (all users) or improve collaboration UX (premium users)? Engineers optimize queries, designers rework flows, QA tests, rollout in stages. Two or three weeks gone, with fifteen people involved.

Now swap in an agent. It doesn’t just monitor—it correlates: adoption drop tied to one UI tweak, on one browser, in one workflow. It quantifies impact: “this fix lifts $8K in expansion revenue.” It suggests solutions: streamline UI here, optimize database pooling there. And it doesn’t stop at advice—it implements, tests, and rolls out with self-healing logic if error rates climb.

Suddenly the cycle shrinks from weeks to minutes. Humans aren’t fire-fighting; they’re reviewing, approving, and steering strategy. Instead of chasing down logs, they’re free to think about the roadmap and customers.

🚀 Key Transformation Benefits

Traditional Approach Limitations:

• Reactive: Problems discovered after significant user/revenue impact
• Time-intensive: Manual analysis creates 2-4 week resolution cycles
• Resource-heavy: Multiple teams required for each issue
• Limited scope: Human analysis covers samples, not comprehensive data

Agentic Approach Advantages:

• Proactive: Issues predicted and prevented before user impact
• Instant Resolution: Minutes vs weeks for problem-to-solution cycle
• Resource Optimization: Humans focus on strategy, agents handle execution
• Comprehensive: 100% data coverage with correlation insights impossible for humans to process

Conclusion

The shift from reactive analytics tools to proactive agentic systems will transform many businesses from firefighting mode to strategic growth acceleration.

Let’s stop the grunt work and become intelligent. Save hours of watching sessions, finding bugs. Let the agents do this for you. 

Check how Zipy will automate the job of watching millions of sessions and tell you what’s wrong. Check how companies are moving from reactive tools to a proactive agentic solution with Zipy.