Research notes from agent developers
How we think about building, deploying, and governing AI agents inside the data workspace. Engineering insights, industry analysis, and original quantitative research from the team shipping Deepnote.
Industry reports
To build a pioneering product, our team conducts industry studies to understand and anticipate the needs of the market. Get access to exclusive reports.
The state of agent readiness 2026
An evidence-based benchmark of 4,350 companies across 22 industries, scored on what we can verify in their technology, hiring, teams, and deployments, not what they announce. Median score: 37.7. Only 1 in 27 companies is AI mature.
4,350
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benchmarked
22
industries
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37.7
median
score
Research
The state of agent readiness 2026
Scoring the evidence, technology, hiring, teams, and deployments behind 4,350 companies’ AI claims.
01 | The landscape
Readiness is bottom-heavy
1 in 5 is Operationalizing | 1 in 27 is AI mature.
02 | Talent & controls
People lag the stack
70% of companies are weakest on talent & controls.
03 | Say-do gap
Announcements vs production
Of 3,075 AI announcements, 2.8% report a production deployment.
04 | Company size
Readiness falls mid-market
2,000–10,000 employees is the market’s low point.
The state of data leadership 2026
A quantitative study of ~40K data, analytics, and AI leaders across 140+ countries. Who leads data today, where they came from, where they go next, and how organizations are absorbing agentic AI.
~40K
leaders
analyzed
140+
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represented
Research
The state of data leadership 2026
Mapping every major flow between ~15k companies and the careers of ~40K data leaders across 140+ countries.
01 | Career velocity
Speed to director by education
This major hits director 2.2× faster than CS/IT grads.
02 | AI era takeover
Leaders with AI/ML in title
10.5 → 34.9% in seven years | +232%.
03 | What drives promotion
Effect on promotion probability
Masters+ +1.4pp | Geo move +0.8pp | Tenure +0.3pp.
04 | Gender across the ladder
Share of women by seniority
Manager 38% → C-suite 23% | monotonic.
Development insights
Sharing our learnings from our forward-deployed engineers who help leading companies building data workspaces for agents & humans safely and scalably.
Your data team is already building a context layer. They just don't call it that.
How separating planning from execution — with cell-level reactivity — lets the agent self-correct without touching production data.
Jakub Jurovych
Apr 28, 2026
11 min read
How we used Claude Opus 4.6 to fix failing E2E tests
How our QA agent triages, reproduces, and patches Playwright failures — and the prompt patterns that made the biggest difference.
Tomas Kislan
Apr 14, 2026
9 min read
Data notebooks as the atomic unit for RL
Why a reactive notebook — with its data, code, and outputs in one place — is the right atomic unit for training and evaluating RL agents.
Jakub Jurovych
Mar 31, 2026
7 min read
AI briefings & outlook
Curated bi-weekly roundup of how AI is reshaping the industry, academic signals, and the latest on AI agents, LLMs, and AI infrastructure.
Doomsdayers, rejoice. If the rumors on X hold up, DeepMind may have cracked recursive self-improvement: AI building ever-better AI.
Data Deep Dives
Deepnote's Substack
When models are interchangeable, the money moves to whoever picks between them.
Data Deep Dives
Deepnote's Substack
Plus, an open policy problem
Data Deep Dives
Deepnote's Substack