Pet Planner is a real multi-platform SaaS, running in production. But what it proves is not on the user's screen — it is in how it was made: 10 AI agent personas, a 7-phase pipeline with human approval between each one, and a cost log that records token by token. This is not a pet app. It is proof that the agent factory delivers.
To understand the telemetry, first the object. Pet Planner is a multi-platform pet health and routine SaaS for pet parents in Brazil, delivered as a PWA on the web and a native app on mobile, both on top of the same API. Five repositories, one product — and this page is about how they were built.
OpenAPI and AsyncAPI events are the end-to-end source of truth. The same contracts become contract tests in CI for API, Web and Mobile — the PWA and Flutter speak the same language because they validate the same contract.
Encrypted PII (AES-256), per-IP rate limiting, mandatory e-mail verification and a leaked-password blocklist. It is not a patch applied later: the agents follow this posture as part of the organization's standard.
Pet Planner's AI is in the build, not in the runtime. The product's API ships no AI — no LLM answers the end user. It is an app built by AI agents, not an app with AI features. Saying that out loud is part of the brand: where there is AI, we point at it; where there is none, we say so.
The AgentWorkspace is a spec-driven multi-agent environment. Each persona has a defined role; none works alone and no phase moves forward until a person approves. It is the ZDZCloud method in practice: copilot first, AI + People, evidence-based decisions.
The Product Owner and the Architect turn the story into a specification: contracts, acceptance criteria and ADRs. The source of truth is born here — not in the code.
The Orchestrator designs the execution plan and syncs it with Azure Boards. The work becomes an auditable map before any agent writes code.
The plan is broken down into parallelizable tasks, each with a clear contract and scope — ready to be routed to the right devs.
API Dev, Web Dev and Mobile Dev work in parallel on the same contract. Code and tests ship together; the contract tests keep the three clients aligned.
Reviewer, QA and Security Auditor go over everything with a fine-tooth comb: quality, coverage and security/LGPD posture. It is where the machine challenges the machine — and the person arbitrates.
With the gates green, the delivery is merged into the base. Contracts and ADRs remain as the record of what was decided and why.
DevSecOps drives the release. The cycle closes with the story in production — and its token cost log already accounted for.
Every campaign talks about "AI savings". We show the log. These are the real numbers from one 8-story sprint of Pet Planner — savings measured, not estimated. It is the same auditability instinct we apply to code: if we cannot point at the number, it does not go in.
These 3.28 million tokens were spent building Pet Planner: writing spec, code, tests and reviews. None of them runs in the app the user opens — the AI is in the BUILD, not in the product's runtime. Measuring the cost of building is what lets us say that with numbers in hand.
Pet Planner is not a one-off assembled for the portfolio: it is the agent factory running the way it runs every day. What you saw here — the fleet, the gates, the cost log — is the process that goes on to build your product.
API, Web and Mobile devs work in parallel on a single contract. Several streams move forward at once without going off the rails, because they all validate the same source of truth.
Seven phases, human approval between each one, ADRs and contracts as the record. You approve, reject or request changes — the weighty decision always belongs to a person. AI + People.
Every story has its token cost log. You do not get an opaque "AI" invoice: you get the number — calls, tokens, cache hit — to decide with evidence.
71% of input tokens came from cache. Structuring the work to reuse context is what separates an expensive agent fleet from a viable one. Cache is an architecture decision, not luck.
A gate between each phase keeps a spec mistake from becoming deploy rework. The person does not slow the fleet down: they keep the fleet from racing in the wrong direction. Copilot first, autonomy proven later.
Without the cost log, "AI saves money" is faith. With it, it is a spreadsheet. Instrumenting tokens per story turns the AI cost discussion into something auditable — and honest about where the AI is and where it is not.
Multi-platform on one contract, an agent fleet doing the development, human approval between phases and every token measured. Let's talk about your project — and show you the process, not just the slide.