The Agentic Engineer's Playbook: From Prompting to Patterns
Stop fighting with AI tools and start architecting with them. Most engineers are stuck in prompt-and-pray mode, but the real productivity gains come from systematic workflows that turn AI from a frustrating chatbot into a strategic partner.
This talk reveals three battle-tested patterns that will fundamentally change how you build software. You'll see exactly how to compress months of learning into days, why your long ChatGPT conversations always derail (and how to fix it), and discover the "scratchpad" technique that prevents you from making the same mistakes twice.
- The Iterative Workflow — Learn why well-formed problems beat clever prompts every time, and see the strategic framework for choosing between Cursor, Claude, and Gemini based on what you're actually trying to accomplish.
- Context Management — Master the art of keeping AI conversations productive with "Clean Slate" resets, "Summary Carry-over" techniques, and the game-changing "Scratchpad" method that maintains institutional memory across projects.
- The Onboarding Accelerator — Watch a real case study of mastering Google's Agent Development Kit in record time by pointing AI at codebases and asking the right architectural questions.
This isn't about productivity hacks—it's about becoming an architect of AI-augmented workflows that amplify your engineering expertise and accelerate your entire team's capability.
Tanya Dixit
Tanya Dixit is a Machine Learning expert at Google who helps engineering teams across Australia, India, Singapore, and Europe build better AI solutions. Before Google, she served as a Machine Learning Engineer at Crayon, where she led the development of scalable AI deployment and monitoring frameworks, contributed to generative AI infrastructure (LLMOps), and delivered enterprise-grade solutions across cloud platforms. Tanya earned her Master's in Machine Learning and Computer Vision from ANU, graduating with First Class Honours and snagging the University Medal along the way.
When she's not building AI systems, Tanya loves mentoring at startup accelerators, writing AI newsletters that cut through the hype, and speaking about how to make technology more inclusive. Her thing is helping companies figure out the right problems to solve with AI—because let's be honest, the tech is only as good as the problems you point it at. She's big on the idea that collaborative intelligence can make entire engineering teams (and communities) way more effective.