# Jiggy Kakkad & Tinus Willemse — AI Engineer Sydney 2026

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Canonical page: https://webdirections.org/ai-engineer/speakers/tinus-willemse/
Program status: The speaker lineup and talk descriptions are public. Session days, times, rooms and the full timetable have not yet been published.

## From Prompt Rules to Structural Guarantees: The Harness Behind a Production Analytics Agent


Checkout AI answers open-ended questions about retail sales data in natural language. It plans,
calls analytics tools over MCP, executes Python in a sandbox, and returns a written analysis with
charts. In a system like this, failure is rarely a crash: the chart renders, the prose is fluent, and
an incorrect figure reaches a decision-maker unchallenged.

Prompts define the agent’s behaviour but cannot enforce it. Enforcement is engineered into
the layers around the model, at three points in the lifecycle: a bad answer is caught before it
reaches the user, a bad change before it reaches the codebase, and a bad build before it reaches
production.

Before the user. The agent does not author its own charts. It calls typed tools whose outputs
are validated against data contracts shared with the renderer, so a malformed visualisation cannot
be constructed, let alone displayed. Deterministic validation sits between analysis and synthesis,
and every figure in the narrative is checked against the data the user can actually see.

Before the merge. Fourteen evaluation metrics gate development: golden-case metrics run
on every prompt and plan change, while metrics that need no expected answer score live produc-
tion traffic. Production failures are replayed in a local development harness that reproduces the
full agent stack, and are captured as golden cases before a fix is written.

Before production. Immutable, commit-tagged builds, a single source of deployment truth,
and supply-chain scanning ahead of every merge.

None of these layers required a better model. We close with the case for treating the harness,
not the model, as the primary engineering surface of a production LLM agent.

## Jiggy Kakkad

Staff AI Engineer, Quantium

Jiggy Kakkad is a Staff AI Engineer at Quantium, working on Checkout AI’s conversational analytics
agent. He came to AI engineering from a software engineering background, and spends his time on
harness engineering and automation: the layers around the model that decide what it can do and what
reaches production.

## Tinus Willemse

Executive Manager, AI & Data Science, Quantium

Tinus Willemse is an Executive Manager, AI & Data Science at Quantium, building Checkout AI, a conversational retail analytics product. He works on the agent architecture at its core — planning, running and narrating the analysis, the evals that keep it honest — and on making a non-deterministic system a reliable one.

## Conference

- [Conference overview](https://webdirections.org/ai-engineer/index.md)
- [Agent guide](https://webdirections.org/ai-engineer/for-agents/)
- [llms.txt](https://webdirections.org/ai-engineer/llms.txt)