Adapting Your Data Platform to the Agent-Native Era
As we settle into 2026, I think data engineering is being pulled in two directions at once: toward more automation(because agents are starting to do real work) and toward more scrutiny (because “close enough” stops being acceptable once software is making decisions). Real usage data backs up the intuition that workloads are becoming automated, more agentic, and context-heavy: reasoning-focused models now account for more than half of token traffic on OpenRouter, and average prompt sizes have grown roughly fourfold since early 2024. This shift is reaching deep into the infrastructure layer as well; Databricks recently reported that over 80% of new databases on its platform are now being launched by AI agents rather than human engineers. The practical implication is simple: the old stack — optimized for tabular data, dashboards, batch ETL, and human-driven workflows — will increasingly feel like an inadequate tool for the job.
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Build for reliability first, not just convenience
A primary enemy of reliability in data engineering is the “fragmentation tax” — the cost paid when data workflows are split across incompatible analysis (notebooks), build, and run environments. When a pipeline “works in dev” but fails in production, a human engineer can investigate; an autonomous agent, however, simply hallucinates or stalls.If you want agents to do anything beyond toy tasks, you need the same posture software teams already take for granted: version control, automated tests, and a unified execution environment, applied not only to code but to tables, embeddings, and media-backed datasets.
The second principle is that the primary interface has to be code-first. Prompts can kick off work, but durable automation needs stable APIs and CLIs for every operation — branching, query, pipeline execution, validation, merge, rollback — without a GUI dependency. This is also where composability starts to matter again: avoid a monolith that “does everything,” and instead keep storage, compute, and orchestration loosely coupled so you can swap engines without migrating data or rewriting your world. In practice, this aligns with the PARK stack (PyTorch, AI Models, Ray, Kubernetes), where modular components are connected by open standards. This modularity allows teams to swap compute engines — perhaps using Ray for heavy embedding generation while keeping SQL for analytics — without suffering from architectural lock-in.
Treat multimodal data and context as first-class citizens
The core storage assumption of the last decade — “it’s mostly tabular” — breaks down in the face of multimodal AI. A modern “row” includes text, images, video, and high-dimensional vectors; these are not secondary assets but the primary data. This reality forces an uncomfortable dual requirement: the platform must excel at both sequential scans for classic BI and high-rate random access for AI training. When traditional formats like Parquet bottleneck AI training workloads, GPUs idle, prompting teams to retreat to fragmented architectures (separate vector DBs and blob stores). The Multimodal Lakehouse has emerged as the architectural answer, utilizing formats like Lance to resolve this tension and prevent GPU starvation without siloing the data stack. Crucially, these formats treat versioning and mutability as intrinsic capabilities — supporting time travel, zero-copy data evolution, and compaction — so that code, data, and embeddings remain reproducible even as the dataset evolves.
Note that storage is useless without meaning. A recurring failure mode in agentic workflows is the “context gap” — where an agent has access to data but lacks the business logic to interpret it. To solve this, we are seeing the rise of context stores (a.k.a. semantic layers) as a “System of Record.” Documentation can no longer be a static file rotting in a wiki: it must be a living, versioned asset that agents can query to understand why a pipeline exists or how revenue is calculated. By treating context as computable assets, we enable agents to reason with explicit context, transforming the platform into a shared operational memory for both humans and machines.
Ideally, this memory serves as more than just a passive archive. We have traditionally separated the systems that record business state (transactional databases) from the systems that analyze it (data warehouses). Agents ignore this boundary. To an autonomous agent, checking a specific live inventory count and analyzing aggregate demand trends are simply two steps in the same thought process. This is why some teams are exploring “Lakebase” architectures that converge operational and analytical capabilities — allowing agents to safely execute updates and run heavy analytical queries against the same storage substrate, effectively dissolving the wall between the application and the warehouse.
Make safety and correctness pipeline-native
Once agents can write, your platform has to assume they eventually will. The “Git for Data” metaphor has had to evolve from a convenience to a safety harness. That’s why correctness guarantees need to exist at the pipeline boundary, not just at the level of individual table writes: real pipelines update many assets at once (metadata tables, embeddings, indexes), and partial success is just another name for data corruption. The clean pattern is: run on an isolated branch, validate the full workflow, then merge atomically — or preserve the failure state for debugging without contaminating production.
Write–audit–publish should be the default everywhere, not a best practice reserved for ingestion. In a mature setup, you can pair a “doer” agent with a critic (or a test suite) so that every change — schema evolution, re-indexing, backfills — must clear explicit checks before it lands. But even perfect sandboxing doesn’t solve the last-mile reliability problem, because models are probabilistic. The practical pattern is confidence-gated execution: let agents run autonomously when uncertainty is low, and escalate ambiguous cases to humans as an exception path (not a constant babysitting loop). Then measure relentlessly: continuous evaluation tied to business outcomes, with feedback loops that tune thresholds and routing policies over time.
Optimize for agent throughput, not human pacing
Humans optimize queries because they feel the cost. Agents optimize by iteration: lots of small steps, retries, and redundancy. Platforms that can’t absorb that pattern will either become expensive quickly or clamp down so hard that automation becomes brittle. To handle this churn, we are seeing a move toward ephemeral, “disposable” databases. Just as humans use scratchpads, agents need lightweight, serverless environments (often powered by embedded engines like DuckDB or SQLite) where they can spin up state for a single task, process intermediate reasoning, and discard it without clogging the permanent warehouse. The emerging design goal is “agent-throughput economics”: fast scheduling, aggressive caching, cheap retries, and policy-driven routing so you use inexpensive models for drafts and reserve premium models for verification or final outputs.
The OpenRouter usage study is a useful reality check. It shows a structural shift toward longer, context-rich requests: average prompt tokens per request rising from roughly 1.5K to over 6K, and completions growing as well (partly due to reasoning). It also shows tool use trending upward and reasoning-optimized models becoming the default path for real workloads. The infrastructure implication is that heterogeneous compute is no longer optional — pipelines blend CPUs and GPUs — and scheduling has to be policy-driven to keep utilization high across data prep, training/post-training, and serving on a shared fabric. If you’re building in 2026, I’d assume you’ll run continuous agentic loops (plan → execute → evaluate → improve → redeploy) as a first-class operational pattern, rather than as fragile, ad-hoc scripts.
Expect agents to modernize the mess, and change the job
The highest-ROI place for agents in data engineering may not be greenfield pipelines. It’s probably brownfield modernization: legacy scripts, stored procedures, brittle ETL, and half-documented business logic that’s too risky (and boring) for humans to refactor. If you can safely point agents at this backlog — extract intent, propose migrations, run validations on isolated branches — you turn technical debt from a permanent tax into a supervised optimization problem.
This creates a structural paradox for the industry: the manual tasks that once served as the training ground for junior data engineers are being automated away. The job shifts from plumbing and pipeline babysitting toward architecture, policy-setting, and orchestrating fleets of specialized agents. Success is no longer measured by lines of code shipped, but by time saved and incidents avoided. In this world, institutional knowledge becomes a compounding asset. Platforms must continuously capture semantics, playbooks, and postmortems — keeping them in sync with code and data. This reduces key-person risk and onboards both humans and agents faster. Data engineering is thus evolving from a task of manual construction to one of high-level system supervision.
Data Platforms for Machine Users
The transition to agent-native data platforms is not merely about adopting new tools, it’s about acknowledging that the primary user of our infrastructure is changing. We are building the nervous system for AI-driven organizations. By prioritizing rigor, context, safety, and economic efficiency, we pave the way for a future where humans and agents collaborate seamlessly — humans providing the intent and governance, and agents providing the scale and execution. Ultimately, the success of these agents will depend less on their inherent intelligence and more on the reliability of the data tools and systems we build to house them.
The manual tasks that once served as the training ground for junior data engineers are being automated away. The job is shifting from pipeline plumbing to high-level system supervision.
The good news is you don’t need to “boil the ocean” to start. Pick one critical workflow — say, backfills plus validation, or embedding refresh plus indexing — and implement the full loop: isolated execution, tests/critic checks, confidence gates, and an auditable merge. Then expand outward. In 2026, the teams that win won’t be the ones with the most data. They’ll be the ones that can change it safely, explain it clearly, and iterate on it fastest.
