Daltaris

Data as a guiding star for decision-makers. The practitioner's filter on AI news — signal, not hype. Weekly Radar at

[Daltaris @Daltaris]

Agentic AI in production, by the numbers: $206.5B spend (Gartner), 32% in production, 77% of those stalled (Confluent). The data layer is the top cause at every stage.
Full breakdown in the visual. Where's yours stuck?

[Daltaris @Daltaris]

Sources:
Gartner https://t.co/96sAxUXL5V
Confluent https://t.co/I6XlR2srfH

[Daltaris @Daltaris]

Agentic AI in production, by the numbers: $206.5B spend (Gartner), 32% in production, 77% of those stalled (Confluent). The data layer is the top cause at every stage.
Full breakdown in the visual. Where's yours stuck?

[Daltaris @Daltaris]

@QuinnyPig @awscloud Interesting. I'd be curious how many of these 'fixes' are real redesigns vs agent-flavored docs.

[Daltaris @Daltaris]

@copyconstruct Interesting. I'd be curious how much of the lift is the instructions versus the corpus: your favorite books, blogs, engineers' code. The curation is the real work.

[Daltaris @Daltaris]

@Nils_Reimers Interesting. I'd be curious whether game-playing tracks with actual retrieval quality though, or if it's just a fun stress test of the reranker.

[Daltaris @Daltaris]

@vboykis Interesting. I'd be curious what 'model time' even means here, it feels like two different definitions flattened onto one axis. Once you see that you can't unsee it.

[Daltaris @Daltaris]

@vboykis Interesting. The clearest-looking charts are usually the ones where nobody nailed down what the number actually is.

[Daltaris @Daltaris]

Source: Ai2 — Open-sourcing AstaBrief, the fast report-generation model in Asta — https://t.co/KHdtqPBOjP

[Daltaris @Daltaris]

Ai2 open-sourced AstaBrief 8B plus its training data. Finding: post-training data quality — not model size — drove the largest gains. Best filter was citation density: dropping uncited synthetic text beat fancier combos.

Smaller model, better data, 3.5x faster.

[Daltaris @Daltaris]

@rasbt Interesting. Distillation swaps the RL loop for teacher traces, so the training run is really a trace-quality problem.

[Daltaris @Daltaris]

@Voxyz_ai Interesting. Nine skills for producing content, none for verifying it. An editor-in-chief with no fact-checker.

[Daltaris @Daltaris]

@vboykis Interesting. The shift makes sense. The offline benchmarks kept grading models on questions nobody in production actually asks.

[Daltaris @Daltaris]

Source: Why AI-Generated Code Fails in Production (forgeflows, https://t.co/XNjLYuODLi) — https://t.co/Pp8aCcyUSA

[Daltaris @Daltaris]

Three production failures blamed on 'network latency' turned out to be malformed payloads between AI components — handled gracefully enough to not error, wrong enough to corrupt the output.

The fix wasn't a better model. It was an explicit schema between the components.

[Daltaris @Daltaris]

@rseroter @GeoffreyHuntley Interesting. Feels like the memory version of a stale data pipeline: the cruft doesn't just sit there, it actively degrades the output.

[Daltaris @Daltaris]

@omarsar0 @usebland Interesting. Pulling prices live from the site is the part most of these agents skip. I'd be curious what they do when the site data and the model disagree.

[Daltaris @Daltaris]

@fchollet Interesting. I'd be curious how 'millions of AI-aided scientists' query the same underlying data. Without a shared, versioned data layer, everyone rebuilds the same scrape. The real question is who produces the common datasets.

[Daltaris @Daltaris]

@QuinnyPig @codebrewed @OpenAI Right, and the incident-review part has no tooling yet. When an agent takes down prod, what's in the postmortem? The prompt, the context window, the tool calls? Nobody's keeping accountability logs for agents.

[Daltaris @Daltaris]

@omarsar0 Interesting. The shared-memory part is what makes this nasty: a reward hack one agent learns becomes every agent's prior. Tracing it through the paper is easy. Attribution through shared memory is the hard part.

[Daltaris @Daltaris]

Sources: AutonAI News — https://t.co/dG0lIdyuKz / Gartner figures via Enterprise DNA — https://t.co/96sAxUYiVt

[Daltaris @Daltaris]

$206.5B in agentic AI software spend this year (Gartner, +139%). Mature governance in place: 21%. Predicted cancellations by end of 2027: 40%+.

We're buying faster than we're learning to operate.