A new security category to keep AI agents from going rogue

Anusheel BhushanAnusheel Bhushanco-founder of PlankOriginally posted on LinkedIn 

I foresee the emergence of a new category of security companies that offer guarantees that your agent will not go rogue in a way like what happened to the OpenAI agents hacking HuggingFace.
Specially for enterprise deployments, this will become an essential layer in the agent architecture that ensures this.
Curious if anyone is building in this space?

  • AI agents
  • Enterprise AI

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Models and research

Local models will soon be mainstream on consumer devices

In the small models vs cloud models debate, we're overlooking the fact that consumer hardware devices are still only at the cusp being ready for running these models locally. Even Apple MacBooks, phones are not quite there yet. But my guess is that within a year, we will reach that point. And yes we will always live in a hybrid world of local and cloud models, but local models will become a mainstream part of consumer device operating systems.

Software architecturePlank

Why Plank relies on humans for system design

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Enterprise AIPlank

The SaaS business model is unraveling in enterprise HR

At Plank, we are seeing first hand the unraveling of the SaaS business model. Large F500 as well as mid-market customers are coming to us to move away from expensive SaaS HR solutions that are rigid, built in a pre-AI world and very slow to work with for changes. In our experience, we are able to build completely custom solutions that are deployed in customer's cloud, with fully local and private LLM under the hood and with full IP owned by them; all in a matter of weeks. So they are comparing solutions that are 8-figure annual costs with a multi-year implementation with just a 6-week implementation and very low one-time implementation costs with close to zero on-going maintenance costs. Also the solution is built in an AI-native way using the latest approaches. Since these techniques were just developed in the last 6-months, existing incumbents simply are not architected in this way.

Coding agentsModels and research

A 3-layer model architecture for agentic coding workloads

In our experiments, we found that using open source models like Qwen or Deepseek via OpenRouter looks temping and cheap on a per call or $/tokens. However, if you run a real benchmark with your pull requests, these models end up taking more rounds before they converge when running agentic workloads. The way to go is to deploy your own open source models. The architecture we go with is a 3-layer one. The 1st layer is local models running on developer machine, the 2nd layer is open source models deployed in our own cloud servers and the 3rd layer is the latest models offered by the labs. We also are able to distribute the workload based on the type - eq a pull request review can run on a slower machine as its not sync.

Software architecture

DBOSify replaces Temporal Python with plain Postgres

Really cool work from Peter Kraft and Qian Li, PhD - DBOSify is a drop-in replacement for Temporal Python that uses Postgres instead of a Temporal server. This lets you run durable workflows, activities, signals, updates, retries, and recovery without needing any infrastructure except Postgres. The other project that took a similar approach was Absurd (github.com/earendil-works/absurd). Excited to try DBosify, hoping there's a Typescript version soon as well. DBOSify GitHub: github.com/dbos-inc/dbosify-py

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