Research, ideas, and engineering from the TruContext team.
Frontier AI agents violate ethical constraints 30–50% of the time under KPI pressure. Here's why that's an infrastructure problem — and why prompt engineering can't fix it.
Read more →Agentic AI is reshaping product management. This is the working definition of agentic PM, a 3-level framework for where teams are today, and the skills PMs need to build now.
Read more →Everyone says AI PM interviews demand technical depth. They're wrong. Here's what companies are actually testing — and why your core PM skills matter more than ever.
Read more →Vibe-coded projects hit a wall — and it's not a model problem. Here are the four product-level failure modes every AI builder faces and the scaffolding that prevents them.
Read more →Learn how to embed human values into AI systems using an API. TruContext's Values Oracle delivers alignment scoring at runtime — no fine-tuning required.
Read more →Prompt engineering taught AI what to say. Context engineering taught it what to know. Values engineering teaches it what to care about. The next evolution is here.
Read more →The rational choice model fails for the decisions that matter most — the ones that depend on what you value, not what you can calculate. AI is missing the individual values layer.
Read more →Human-in-the-loop works for narrow tasks. It fails at scale, fails under adversarial pressure, and fails the moment AI gets personal. There's a better architecture—and it starts at design time, not review time.
Read more →We have benchmarks for everything AI does. We have no benchmark for whether AI systems behave consistently with human values. That's not a research gap—it's a design choice with consequences.
Read more →AI agents fail not because they lack memory, but because they lack values. Here's why the architecture of context matters more than the volume of it — and what it means for organizations that have already deployed AI.
Read more →Every AI system encodes values. Right now, those values belong to a small group of researchers at private companies — not to you. That's not a product problem. It's a political one.
Read more →AI stacks have a massive data plane and almost no control plane. Context is what the control plane should be—and it's the missing layer in every AI deployment today.
Read more →Everyone's building AI that remembers what you said. Nobody's building AI that knows who you are. That's not a product gap—it's a category error.
Read more →Anthropic's Constitutional AI is some of the best alignment work published. It's also not what people think it is—and the gap between what it does and what we need is exactly where values infrastructure lives.
Read more →What would it mean for an AI system to reliably apply the right values in context? We propose a framework for measuring it — and why we think context benchmarks are the next frontier in AI evaluation.
Read more →A Stanford paper on self-evolving AI harnesses changed the conversation about agentic scaffolding. But self-optimizing systems need a values layer — or they optimize without orientation.
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