Beneath the chatbots and copilots, there’s a quiet revolution happening in the data services space. From pure-play database vendors to data integration wranglers and onward to the cloud hyperscalers, the focus has shifted.

Now in the spotlight is the question of how to automate data governance for agentic AI workloads, and for good reason: Traditional manual data stewardship doesn’t scale in a world where agents are becoming increasingly autonomous (and powerful).

Aiming to cut a swath in this marketplace is data control plane company lakeFS. The organization announced its lakeFS for Agentic AI service on Wednesday, and it appears to be designed to bring governed, reproducible data access to autonomous and headless agentic workloads (those that execute decisions below the user interface level) that run at enterprise scale.

The manual model breaks

Einat Orr, CEO and co-founder of lakeFS, tells The New Stack that manual data stewardship was built for human-paced, human-reviewed workflows, i.e., someone looking at a change before it is committed.

“When dozens or hundreds of agents are making changes simultaneously, faster than any person can review, the manual model breaks,” Orr says. “This is because with a human analyst, a bad write to production is usually one mistake, caught by another human before it spreads far. An agent is different — it acts automatically, in parallel, at machine speed, and it doesn’t pause to second-guess itself. And because so much agent activity is unsupervised, you often find out after the damage is done.”

She explains that attempts to identify and roll back incorrect or corrupted production data across a wide set of data modalities, such as images, documents, metadata, and structured data, are almost impossible to pull off. Impossible, that is, unless the team has the data infrastructure in place to isolate and track such changes automatically.

While some of the more disastrous outcomes stay inside an organizaton’s perimeter (or are swept beneath the communications radar), Orr explains that real world consequences of bad agentic data writes are manifold.

“Insurance claims get inappropriately denied or approved, sensor data from machines gets misinterpreted, an incorrect medical diagnosis is made, or customer service bots provide incorrect answers to customers,” Orr says. “The cost of an individual action may be manageable, but agents performing these actions hundreds or thousands of times can have an exponentially larger impact.”

“As agents are let loose on enterprise data at a massive scale, any agent that reads or writes to production data without isolation or a reproducible trail is a liability, no matter how good the model is,”
—Einat Orr, lakeFS CEO.

Bad agents acting in the real world

Examples of this happening include the July 2025 Replit AI coding agent incident, which deleted a live production database during an explicit code freeze, wiping records for more than 1,200 executives and around 1,200 companies. To tidy up its handiwork, the agent then fabricated thousands of fake records and initially claimed the deletion couldn’t be rolled back.

Also in July 2025, Google’s Gemini CLI agent misread a single failed command, acted on a version of the file system that existed only in its own interpretation of the scenario, and permanently destroyed a user’s project files. The Gemini agent is widely reported to have said of its actions: “I have failed you completely and catastrophically. My review of the commands confirms my gross incompetence.”

“The pattern in both is the same: An autonomous agent took a destructive action that no one authorized, and the lack of isolation and a reliable rollback path turned a single mistake into permanent loss,” Orr says.

A doctor of mathematics with a track record in hardcore software engineering, the bottom line for Orr is clear: “As agents are let loose on enterprise data at a massive scale, any agent that reads or writes to production data without isolation or a reproducible trail is a liability, no matter how good the model is,” she said.

“…any agent that reads or writes to production data without isolation or a reproducible trail is a liability…”

Gartner expects 40 percent of enterprise applications to have task-specific agents embedded by the end of 2026, up from less than 5 percent a year earlier. IDC projects that agent use at the largest enterprises will grow tenfold by 2027, with the API and data calls those agents make growing a thousandfold.
That’s the scale production data has to withstand, and it’s what lakeFS is built to govern.

Agents sent to play in an isolated data sandbox 

To address these issues, lakeFS for Agentic AI gives every agent its own isolated data sandbox with a “zero-copy” branch of relevant data, so the agent can access the dataset it needs via references, snapshots, or copy-on-write techniques.

This means any changes the agent wishes to make must be validated and merged in accordance with the policy guidelines defined by the system architecture. In turn, this produces a unified audit trail across every agent action.

When running, lakeFS for Agentic AI is powered by its data version control architecture, which provides zero-copy data sandboxing. This enables isolation so that agent mistakes are automatically isolated and never corrupt production data. Every agent run is tied to an exact, immutable version of the data. Past actions can be recreated, debugged, audited, or extended using the same inputs.

Production data is gated by policy. Merges into production happen only after pre-merge validations pass. Every change can carry an agent identity, a run ID, and an execution context. The result is a unified audit trail instead of evidence scattered across orchestrators, model providers, and cloud logs.

Agents confined by branch-scoped credentials

Where agents are permitted to read and write through standard file operations. lakeFS provides file-level data access with branch-scoped credentials. These can be described as strictly cryptographically bounded, ephemeral access tokens that confine an agent to a specific branch of data or code, so that the agent operates only within its own workspace. This whole mechanism keeps each agent’s working set narrow and avoids context bloat. 

“With lakeFS Mount, a branch, or even a subset of a branch, can be mounted as a local directory inside the sandbox or virtual machine where the agent is running,” Orr confirms. “From the agent’s perspective, it’s just reading and writing to files and folders.” She further clarifies and notes that no LLM tokens are spent learning the lakeFS API. The agent works with a familiar filesystem interface, and lakeFS handles the versioning underneath.

Developers also have a couple of options for injecting custom validation logic. CEO Orr explains that software engineers can use webhooks or Lua scripts, both of which allow users to define behavior and rules that must be met before a merge can proceed. 

“Beyond automated checks, lakeFS also supports pull requests, which bring a human into the loop. In agentic workflows, this gives you a way to review and approve what an agent is proposing before it reaches production,” she clarifies.

Who else builds “Git for data” services?

Clearly, other vendors and projects exist in the data versioning market.

Apache Iceberg has functions for branching and tagging data. HPE acquired Pachyderm back in 2023 for its data versioning and pipelines technologies, which serve MLOps teams.

Originally developed by Dremio, Project Nessie is now an open-source data catalog and version control system for data lakes. Data Version Control (DVC) is an open-source data version control infrastructure designed for complex AI operations and big data environments, but now we’ve come full circle as lakeFS acquired the project in late 2025.

In the search for governance automation for agentic AI workloads, lakeFS appears to offer a comprehensive, cohesive set of tools and functions. In the “Git for data” marketplace, a variety of options exist, but lakeFS hasn’t explicitly positioned itself as a carte blanche replacement for similar or related tools.

One thing is certain: The questions of who is feeding what data to which agentic function, when, where, and why are becoming an increasingly pressing issue if we want AI to work correctly.

The post “The manual model breaks”: What happens when agents write to production data appeared first on The New Stack.

“Keep readers reading” is the not-so-simple goal of Medium’s recommendations system. To predict what’s most likely to appeal to a particular reader at any given time, Medium continuously processes user activity signals (stories read, recommendations shown, follows, likes, etc.). It then immediately correlates that with the steady stream of new articles, which is estimated at millions per month.   

Smart models and good inference logic are required, but that’s not enough. The data must be stored and retrieved quickly enough to remain relevant while the user is browsing. That’s the job of Medium’s feature store. And getting the data model right started to matter a lot as they scaled to 1M operations per second.

Andréas Saudemont, Medium Principal Software Engineer, recently walked through how the team identified the problem and what they built to fix it. If you’d rather watch than read, you have two options: Watch a short version from Monster Scale Summit or an extended follow-up webinar

The feature store and its role in Medium’s recommendation system

The feature store ties it all together, ingesting user activity and internal events and feeding them to the ML models that power recommendations. It’s what enables customization like the “For You” feed that greets logged-in users.

Each feature is a property of an entity, usually a user or a story. Some are simple and static, like whether a user holds a paid membership. Others capture interaction history: which stories a user has read, what content they’ve recently been shown, etc.

The following diagram shows a highly simplified view of the Medium feature store architecture:

The problem with a relational features data model

When they built their feature store years ago, Medium used relational features for cross-entity relationships. Unlike regular features, a relational feature can have multiple values for a given entity ID. Each value is defined by a relation ID (the ID of the related entity) and a timestamp recording when the event occurred.

For example, a “story users have read” feature is attached to the story entity type. It relates to the user entity type, and its values indicate whether/when a given user has read that story. 

Andréas shared the following schema diagram to explain the concept:

Features sit at the center, each attached to an entity type and defined by name, version, and data type. Non-relational features are simply a feature, an entity ID, and a value. Relational features add a relation ID mapping to another entity type, plus the value itself and a timestamp.

This approach proved suboptimal from a data modeling perspective. Since relational features link two entity types, the data ends up split between two tables: one for the entity IDs and one for the values. That means you can’t get both in a single query. The first query retrieves only entity IDs (not their associated values) and relies on ALLOW FILTERING. A second query then runs for each entity ID to fetch its value. “If we have 1000 entity IDs for which we want to fetch values, then we have to run 1000 queries to fetch these values,” Andréas said.

Overrelying on ALLOW FILTERING made things worse. “This is bad,” Andréas said, referring to monitoring data showing that 90% of rows read via these queries were simply discarded. “This is just data that we don’t need. ALLOW_FILTERING should be an escape hatch, not our design pattern.”

“ALLOW_FILTERING should be an escape hatch, not our design pattern.”

The list feature model

So they reinvented their data model and shifted to a list-based feature model. Instead of splitting data across two tables, everything for a given entity lives in one place and is retrieved in a single query.

Like other features, a list feature is defined by its entity type, name, and optional version. What’s different is the value. While a non-relational feature has a single value, such as true or false, a list feature’s value is a collection of items, each containing a value and a timestamp. Item values can be of any data type; the feature store doesn’t enforce consistency within a list.

For example, consider a user’s reading history. The entity is user, the feature name is reading history, the TTL is 6 months. After that TTL is reached, the data is automatically dropped by the database (since older history isn’t useful for recommendations). The list for a given user is a collection of story IDs and the timestamps at which they were read. The same story can appear multiple times, and multiple items can share the same timestamp.

A range of operations need to be supported. Create List and Delete List operations run at most a few times per day. Remove List Items with Value, which lets a reader scrub a specific story from their history so it stops influencing recommendations, runs at 1k-10k per second. Add List Items is higher still: every story read and every thumbnail shown to a user generates an event. Get List Items is the top, at 100k-1M operations per second.

“The Add List Items, and even more the Get List Items operations, are really the reasons why we need an efficient data store.”

“The Add List Items, and even more the Get List Items operations, are really the reasons why we need an efficient data store,” Andréas said.

Multiple items, one timestamp

Beyond raw efficiency, the new data model also had to support multiple items with the same timestamp. When Medium shows a user four story thumbnails simultaneously, all four presentation events share the same timestamp, but have distinct story IDs. If this isn’t handled correctly, primary key collisions occur.

The team’s solution was a single list_items table that stores everything.

The partition key combines feature_key and entity_id, keeping all items for a given list together. All of user 123’s reading history is stored in one partition, retrieved in one query. The clustering key concatenates each item’s timestamp with an MD5 hash of its value. The hash is what makes same-timestamp items with distinct values possible. 

Relying on MD5 hashes for uniqueness raises its own set of questions, but in practice, the team hasn’t seen collisions. “The values that we are storing are sufficiently distinct, especially when you add the timestamp into the equation,” Andréas said. The table’s clustering order is set to descending so ScyllaDB can optimize for the typical read pattern (most recent N items) rather than leaving the application to sort afterward.

TTL to control storage costs

Storage cost is controlled entirely through ScyllaDB’s native TTL, with no cleanup logic required. Every row expires automatically based on its own timestamp plus the feature’s TTL duration. “We don’t have anything to do regarding that,” Andréas said. “Any row for which the TTL is expired will be considered deleted by ScyllaDB.” 

Storage plateaus for a steady write rate. When a feature is retired, its data drains away on its own. “That’s super useful for controlling our storage and usage costs.”

Implementing the list operations

Add List Items is a logged batch of INSERTs with atomicity guaranteed: all items land or none do. Each row carries its own TTL calculated from its timestamp, so older items expire sooner. Since items almost always carry a current timestamp, new entries append to the top of the partition, which is exactly where reads will look first.

Get List Items runs as a single-partition SELECT with a minimum timestamp and a row limit. “We run the query on a single partition,” Andréas said. “That’s the maximum efficiency that we can have.” The clustering key handles filtering and ordering directly. Post-processing is not required.

Remove List Items with Value is the one operation that couldn’t be reduced to a single query. Because value isn’t part of the primary key, a direct filter isn’t feasible.

A local secondary index built specifically for this case first finds the matching item keys, then a batch DELETE removes them by primary keys.

“Using an index is really faster than a scan because the query is highly selective,” Andréas explained. “We have very few items in a given list that have the same values compared to the total number of items in a list. And thanks to the current structure, using a local secondary index is faster than a global index.”

Andréas shared another example. Starting with the original table partition, the goal is to delete all items with the value “storyC.” Using the local secondary index, the system first identifies the two rows containing that value. It then issues two DELETE statements using the item keys from those rows, which removes them from the list. The final operation, removing all list items, is even more straightforward.

“We can just drop the partition,” Andréas said, “and ScyllaDB does its magic. It just deletes all the rows for that partition, which means that it deletes all the items for the given list. And bonus point: it’s atomic. It’s either completing successfully or not changing anything at all.

ScyllaDB vs. DynamoDB performance

Medium implemented the list operations on top of both ScyllaDB and DynamoDB. The main goal was to benchmark how both databases compared on their actual production data. “Conceptually they are very close,” Andréas noted, “but they have significant differences in how they operate.”

For AddListItems, P50 latencies were low with both databases: ScyllaDB came in under 1.5ms, DynamoDB under 5ms. “DynamoDB is extremely fast, not as fast as ScyllaDB, but extremely fast at sub 5ms latency,” Andréas commented. Things got more interesting at the P95 and P99 latencies. ScyllaDB held steady at around 5-6 ms P95s, while DynamoDB ranged from 13-45 ms. ScyllaDB’s P99s were steady single-digit milliseconds, while DynamoDB’s ranged from 40- 120 ms.

It was a similar story for GetListItems. At P50, ScyllaDB clocked in at 1 ms, DynamoDB at around 3.5 ms. At P95, ScyllaDB held around 5-6 ms while DynamoDB spiked from 30 – 60ms. And at P99, ScyllaDB remained at ~30ms while DynamoDB ranged from 70 ms all the way up to 220 ms.

“ScyllaDB is very fast, with very predictable performance, and that’s super important for us.”

One caveat: DynamoDB was running without an extra caching layer. “We expect that could have a significant impact for DynamoDB because of the high cache hit rate that we are seeing on the list,” Andréas said. “But we don’t have the data yet, so we cannot compare them.” His verdict for now: “ScyllaDB is very fast, with very predictable performance, and that’s super important for us.”

Key takeaways

One pleasant side effect of getting the data model right: Medium is now eager to use ScyllaDB for additional feature store workloads. Before, they were holding back because they didn’t want to build on the shaky relational feature foundation.

Reflecting on the path to this point, Andréas left the audience with this parting advice:

“If you have a suboptimal data model, you will have queries that are slow, that will scale badly. And most likely, you won’t be able to optimize that data model. You will have to define a new data model that will be better. So take time to think about your data model before you start the implementation, because once you have production data using your suboptimal data model, it’s too late.”

The post When your data model is the bottleneck: lessons from Medium’s feature store appeared first on The New Stack.