Laid off? In between positions? Take advantage of our Open-To-Work discount.
The Data Day Texas 2026 Sessions
We just now beginning to publish the sessions info. Expect to see many session abstracts added over the next few weeks.
Context > Prompts: Context Engineering Deep Dive
Context Engineering is the art and science of designing and managing the information environment that enables AI systems to function reliably at scale. This technical session examines why focusing on prompt engineering alone leads to production failures and demonstrates how proper context management transforms unreliable AI into systems that actually work. We'll explore the fundamental difference between crafting instructions (prompts) and engineering what the model actually processes (context). You'll understand the four core activities of context engineering: persisting information outside the context window, selecting relevant information for each step, compressing histories to manage token limits, and isolating contexts to prevent interference. The session covers critical failure modes including context poisoning where errors compound over time, context distraction where historical information overwhelms reasoning, context confusion from irrelevant data, and context clash from contradictory information. We'll examine why these failures are inevitable without proper engineering and demonstrate specific techniques to prevent them. Through architectural patterns, we'll review context management in existing frameworks. You'll see how declarative approaches eliminate prompt string manipulation, how vector databases enable semantic memory, and how orchestration platforms coordinate context flow.
Observability, Evaluation, and Guardrails for Self-Optimizing Agents
As AI workflows and agents transition from experimentation to production, ensuring reliability, safety, and continuous optimization becomes crucial. Yet most projects begin with prompt engineering or model selection, without an observability and evaluation framework in place. This leads to brittle systems and missed opportunities for improvement. In this session, I'll explore how to build self-optimizing agents by integrating a monitoring framework for observability and evaluation with DSPy, a powerful framework for structured AI workflows. I'll cover why metrics matter, what to measure, and how evaluation outputs themselves can become the training data that drives optimization. You’ll see how real-time datasets generated from evaluations can be used to trigger optimization workflows. For example, when agent performance trends downward (e.g., task usefulness scores dropping below a threshold), prior high-scoring examples can be injected into DSPy workflows to optimize behavior in real time. I'll walk through a live demonstration: monitoring a DSPy workflow, observing metric trends, and triggering an optimization workflow when a guardrail is crossed. The session will close with a discussion of future directions for observability-first AI development.
LLMs Expand Computer Programs by Adding Judgment
Even if large language models (LLMs) stopped improving today, they still have yet to make their real impact on computer systems. This is because LLMs expand the capability of programs by adding judgment. Judgment allows computers to make decisions from fuzzy inputs and specifications, just like humans do. This talk will cover the ramifications of adding judgment to computer systems. We will start with simple judgment-enabled functions and then move to workflows. Adding judgment represents a fundamental shift in how programming is done because instead of writing computer code, system designers will be writing higher-level specifications. We will discuss how these specifications should be written to build robust systems given the sometimes-flakey judgment of LLMs.
We will also discuss how the judgment can be expanded by calling external tools. This tool-calling is a step toward agency, and the talk will also flesh out what agency really requires and the opportunities that it creates. Programs with agency can evaluate their own outputs and therefore improve them, leading to better outputs, which can then be improved. This virtuous cycle enables computer systems to begin to reflect biological ones, possibly even leading to conscious machines.
Data Governance Keynote
Existence over Essence? Data Governance in times of AI
Data governance lies at the heart of socio-technical systems. This includes the foundation for how organizations can integrate automation, AI, and AI agents. As organizations shift their focus in AI from innovation to adaptation, the challenge extends beyond traditional governance to include ethical, strategic, operational implications of autonomous, probabilistic systems. AI is no longer only a tool but a force that is changing the composition of socio-technical systems, reshaping human-machine interactions, accountability structures, and organizational decision-making.
Data governance must refocus on its core and embrace the roles it has to play:
1. Data Negotiation, aligning business, regulatory, technological demands with data requirements.
2. Data Direction, providing strategic orientation to ensure data and AI contribute to organizational purpose and values.
3. Data Audit, embedding accountability across human and machine decision-making.
The move from essence (data as static value) to existence (value through context and application) reframes governance as a force that is intentional structuring socio-technical systems. To operationalize AI at scale, organizations must unify data and AI governance, embedding transparency, fairness, and human oversight into adaptive feedback loops. Ultimately, governance is less about controlling and more about shaping organizational meaning, ensuring that AI amplifies human agency rather than eroding it.
How to Hack An Agent in 10 Prompts: and other true stories from 2025
There's never been a better time to be a hacker. With the explosion of vibe coded solutions full of vulnerabilities and the power and ease that LLMs and Agents lend to hackers we are seeing an increase in attacks. This talk dives into several vulnerabilities agent systems have introduced and how they are already being exploited.
Rewriting SQLite in Rust. Here's What We've Learned.
SQLite is everywhere. SQLite is the most successful database ever built. Three billion installations, maybe more. It's on your phone, in your browser, running your message store.
It's also a synchronous, single-machine system designed when the web was young. No async I/O. No replication. No network protocol. This was fine—until developers started wanting to run it at the edge, sync it across devices, scale it to millions of databases.
In this session, Glauber Costa will show what it takes to make SQLite work for systems it was never meant to handle.
Not because SQLite is broken—it's not. But….
You can't bolt async onto a synchronous codebase.
You can't add memory safety to 150,000 lines of C without rewriting it.
And if you're going to rewrite it anyway, you might as well rethink the whole thing—which is what Glauber and his team at Turso have done. They are rewriting SQLite in Rust.
Glauber will share how they started with libSQL—a fork that's running production systems like Astro DB right now—and then dive into Turso: native async, deterministic simulation testing, the whole database reimagined for distributed systems.
You'll see how deterministic simulation testing finds bugs traditional testing misses. Why async matters even for local queries. What the actual technical tradeoffs are when you're trying to preserve compatibility while changing everything underneath. Where the edges are—what works, what doesn't, what they're still figuring out.
DataOps Is Culture, Not a Toolchain
DataOps is often framed as a collection of tools. In practice it is a culture and a set of engineering behaviors adapted from software and platform teams to the realities of data work. This talk explores the cultural foundations of DataOps, including continuous improvement, learning from failure, blameless retrospectives, and measurement. We will explore the difference between DataOps and DevOps, then define what good measurement looks like for data teams. We will map DataOps outcomes to DORA while also drawing from SPACE and DevEx to capture satisfaction, collaboration, cognitive load, and flow. You will leave with concrete rituals, metrics, and anti-patterns to watch for.
Your Skills, Your Business: Layoff-Proof Your Career through Solopreneurship
The tech industry has taught us a hard lesson: no job is guaranteed. But here's the good news: as a data scientist, you already have everything you need to take control of your career and make it layoff proof.
In this talk, we'll explore how to transform your data expertise into a solopreneur business. You'll get an overview of the solopreneurship landscape, including the different paths available to data scientists and engineers: consulting, freelancing, fractional employment, creating digital products, and more. We'll cover how to start thinking about the financial viability of this model, how to create the actual business, where to find your first clients, and how you can even take those initial steps while still employed. By the end you will walk away with a clearer picture of what's possible and practical first steps to start exploring your options today.
Data Day Texas 2026 is made possible by the generosity of our Patrons and Partners.
These organizations support the data community by making Data Day Texas accessible to practitioners at all career stages.
