Artificial intelligence (AI) isn’t just writing text or generating images anymore. It’s starting to make real-world decisions. Now, with agentic systems, we’re entering an era where AI models don’t just respond; they act autonomously, buying, booking, and negotiating on behalf of users.
That may sound promising, but those of us in the trenches of reliability know that progress always comes with trade-offs. Make no mistake, this shift fundamentally changes how observability works.
Google’s Agentic Payments Protocol (AP2) is one of the first open frameworks designed to enable AI agents to handle end-to-end commerce transactions from product discovery to checkout.
Instead of a human clicking through a shopping cart, an AI agent (powered by Gemini) can:
Behind this, a network of microservices, including merchant, payment, and credential agents, coordinates the transaction
Each agent communicates over JSON-RPC(Remote Procedure Call), using the AP2 spec to ensure trust and interoperability between systems. Essentially, all agents in AP2 act like modular teammates that “speak the same language” (JSON-RPC) and follow a shared playbook (the AP2 spec).
This lets a shopping assistant, merchant, and payment provider coordinate securely, even if they’re built by different companies or run in different environments. By enabling coordination across organizations, AP2 shows how AI can now not only guide but also perform commercial transactions.
That new autonomy comes with a price: blind spots. Traditional synthetic tests assume deterministic logic. You click, the app calls an API, and you measure latency and response codes.
But in an agentic architecture:
In other words, once AI starts making the decisions, traditional visibility breaks down — you lose the map. How do you monitor a system that decides, learns, and acts dynamically across infrastructure you don’t fully control?
To answer that question, we built a functioning AP2 environment using Google’s open-source agentic-commerce/AP2 repository.
Here’s what we stood up locally:
In short, a complete AI-driven commerce stack, from intent to reasoning to payment initiation, running autonomously. At that point, we leveraged Catchpoint Internet Performance Monitoring (IPM) tools to measure performance, track latency, and identify failures across the system.
We modelled the system using Internet Stack Map to visualize and test every layer.
| Layer | What It Represents | Monitoring Method |
|---|---|---|
| DNS (Cloudflare) | DNS resolution and routing for the domain | DNS Monitor |
| CDN (Cloudflare Edge) | Edge delivery, caching, and TLS | CDN/HTTP Monitor |
| Origin (AWS) | FastAPI service entry point | Web Object Monitor |
| Backend (AP2 Agents) | Merchant, Payment, and Credentials microservices | API Monitors |
| AI Layer (Gemini) | Generative reasoning endpoint | API Monitor with response validation |
| UI (AP2 Dev Interface) | Frontend experience and intent submission | Full Browser Test & Transaction Tests with response validation |
To connect these layers, we used Catchpoint’s global variable extraction to capture the session ID generated in the UI synthetic test and reuse it across multiple backend API tests. Tests ran every 5 minutes from global agents.
The UI synthetic flow traces a user journey from the browser prompt (“Help me buy a coffee maker”) through the multi-agent orchestration, cart creation, payment mandate signing, and final payment receipt. Using Catchpoint synthetic E2E flow testing and Internet Stack Map, we get the following:
Transaction correctness: verifies that intent → cart → payment_mandate → signed_mandate → payment_result flows and that the transaction state is persisted across agents.
Per-service performance: measures response time for each service / agent (create_intent_mandate, find_products, create_cart_mandate, get_payment_methods, create_payment_mandate, sign_mandates_on_user_device, initiate_payment). This converts “who’s slowing down the transaction” from guesswork to data.
In the example below, we see find products alone took 13 seconds to fetch all product SKUs.
Token consumption visibility: tracks model token usage (prompt, completion, total) per run so you can spot cost anomalies or prompt drift.
Waterfall & Explorer insights: waterfall traces and explorer request/response bodies surface the exact failing/requesting call and payload, enabling fast RCA
Stack map correlation: connects failing runs to observed infrastructure components (CDN, edge, middleware, origin) to locate the root cause quickly.
Catchpoint’s end-to-end AI commerce monitoring surfaced a few real-world failures that would otherwise appear as generic “assistant unresponsive” events.
1. Gemini errors
When we began testing, the system looked healthy and all endpoints returned 200 OK. But deeper inspection revealed Gemini 429 errors hidden within successful responses.
2. UI stuck due to merchant service timeout
3. UI Down – 530 error from Cloudflare
AI assistants are multi-layered systems where a “slow model” isn’t always the problem. By combining synthetic testing, end-to-end performance tracing, and cross-layer correlation, teams can pinpoint exactly which agent or network segment caused a stall—whether it’s a Gemini model delay, a merchant service timeout, or a CDN origin error.
As AI systems begin to transact independently, observability must extend beyond servers and APIs into the logic that drives decisions. With IPM, organizations can now monitor AI commerce environments across both the infrastructure and reasoning layers, ensuring reliability in this new class of intelligent systems and provide teams with visibility into:
Agentic systems like AP2 are early prototypes of a broader shift, where applications become autonomous agents orchestrating workflows in real time. When that happens, performance data will no longer be just about page loads or API speeds. It will be about intent execution, the AI’s ability to fulfil a task successfully across systems. At Catchpoint, that’s the challenge we’re already preparing for: extending observability from systems to intelligent decision flows.
Below are real interactions highlighting both a smooth and an error-prone client experience in agentic commerce.
When AI agents buy, book, and negotiate autonomously, observability must evolve. Using Google’s AP2 framework and Gemini model, we built a full agentic commerce stack and instrumented it end to end. The findings reveal where visibility breaks across LLM reasoning, microservices, and networks, and how synthetic testing and Stack Map restore traceability and root-cause insight.
Artificial intelligence (AI) isn’t just writing text or generating images anymore. It’s starting to make real-world decisions. Now, with agentic systems, we’re entering an era where AI models don’t just respond; they act autonomously, buying, booking, and negotiating on behalf of users.
That may sound promising, but those of us in the trenches of reliability know that progress always comes with trade-offs. Make no mistake, this shift fundamentally changes how observability works.
Google’s Agentic Payments Protocol (AP2) is one of the first open frameworks designed to enable AI agents to handle end-to-end commerce transactions from product discovery to checkout.
Instead of a human clicking through a shopping cart, an AI agent (powered by Gemini) can:
Behind this, a network of microservices, including merchant, payment, and credential agents, coordinates the transaction
Each agent communicates over JSON-RPC(Remote Procedure Call), using the AP2 spec to ensure trust and interoperability between systems. Essentially, all agents in AP2 act like modular teammates that “speak the same language” (JSON-RPC) and follow a shared playbook (the AP2 spec).
This lets a shopping assistant, merchant, and payment provider coordinate securely, even if they’re built by different companies or run in different environments. By enabling coordination across organizations, AP2 shows how AI can now not only guide but also perform commercial transactions.
That new autonomy comes with a price: blind spots. Traditional synthetic tests assume deterministic logic. You click, the app calls an API, and you measure latency and response codes.
But in an agentic architecture:
In other words, once AI starts making the decisions, traditional visibility breaks down — you lose the map. How do you monitor a system that decides, learns, and acts dynamically across infrastructure you don’t fully control?
To answer that question, we built a functioning AP2 environment using Google’s open-source agentic-commerce/AP2 repository.
Here’s what we stood up locally:
In short, a complete AI-driven commerce stack, from intent to reasoning to payment initiation, running autonomously. At that point, we leveraged Catchpoint Internet Performance Monitoring (IPM) tools to measure performance, track latency, and identify failures across the system.
We modelled the system using Internet Stack Map to visualize and test every layer.
| Layer | What It Represents | Monitoring Method |
|---|---|---|
| DNS (Cloudflare) | DNS resolution and routing for the domain | DNS Monitor |
| CDN (Cloudflare Edge) | Edge delivery, caching, and TLS | CDN/HTTP Monitor |
| Origin (AWS) | FastAPI service entry point | Web Object Monitor |
| Backend (AP2 Agents) | Merchant, Payment, and Credentials microservices | API Monitors |
| AI Layer (Gemini) | Generative reasoning endpoint | API Monitor with response validation |
| UI (AP2 Dev Interface) | Frontend experience and intent submission | Full Browser Test & Transaction Tests with response validation |
To connect these layers, we used Catchpoint’s global variable extraction to capture the session ID generated in the UI synthetic test and reuse it across multiple backend API tests. Tests ran every 5 minutes from global agents.
The UI synthetic flow traces a user journey from the browser prompt (“Help me buy a coffee maker”) through the multi-agent orchestration, cart creation, payment mandate signing, and final payment receipt. Using Catchpoint synthetic E2E flow testing and Internet Stack Map, we get the following:
Transaction correctness: verifies that intent → cart → payment_mandate → signed_mandate → payment_result flows and that the transaction state is persisted across agents.
Per-service performance: measures response time for each service / agent (create_intent_mandate, find_products, create_cart_mandate, get_payment_methods, create_payment_mandate, sign_mandates_on_user_device, initiate_payment). This converts “who’s slowing down the transaction” from guesswork to data.
In the example below, we see find products alone took 13 seconds to fetch all product SKUs.
Token consumption visibility: tracks model token usage (prompt, completion, total) per run so you can spot cost anomalies or prompt drift.
Waterfall & Explorer insights: waterfall traces and explorer request/response bodies surface the exact failing/requesting call and payload, enabling fast RCA
Stack map correlation: connects failing runs to observed infrastructure components (CDN, edge, middleware, origin) to locate the root cause quickly.
Catchpoint’s end-to-end AI commerce monitoring surfaced a few real-world failures that would otherwise appear as generic “assistant unresponsive” events.
1. Gemini errors
When we began testing, the system looked healthy and all endpoints returned 200 OK. But deeper inspection revealed Gemini 429 errors hidden within successful responses.
2. UI stuck due to merchant service timeout
3. UI Down – 530 error from Cloudflare
AI assistants are multi-layered systems where a “slow model” isn’t always the problem. By combining synthetic testing, end-to-end performance tracing, and cross-layer correlation, teams can pinpoint exactly which agent or network segment caused a stall—whether it’s a Gemini model delay, a merchant service timeout, or a CDN origin error.
As AI systems begin to transact independently, observability must extend beyond servers and APIs into the logic that drives decisions. With IPM, organizations can now monitor AI commerce environments across both the infrastructure and reasoning layers, ensuring reliability in this new class of intelligent systems and provide teams with visibility into:
Agentic systems like AP2 are early prototypes of a broader shift, where applications become autonomous agents orchestrating workflows in real time. When that happens, performance data will no longer be just about page loads or API speeds. It will be about intent execution, the AI’s ability to fulfil a task successfully across systems. At Catchpoint, that’s the challenge we’re already preparing for: extending observability from systems to intelligent decision flows.
Below are real interactions highlighting both a smooth and an error-prone client experience in agentic commerce.
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