Tasso Argyros @tasso
entrepreneur & vp eng @ Databricks ··· founder of category defining cos: Aster Data (big data) and ActionIQ (customer data platform) ··· Stanford CS PhD dropout linkedin.com/in/tasso NYC Joined August 2008-
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Congrats! Open source GLM model is really a game changer! Extremely fast, cheap, and high quality!
you may have heard that glm-5.2 at 280 token/s is cool, how about 318 and we still have room to go
One-to-one personalization has long been a goal in marketing. The obstacle was always cost. You could never give every customer their own dedicated team of marketers. Agents change that math. Last week at #DataAISummit, we introduced CustomerLake, an agentic customer data platform that assigns multiple autonomous agents to each customer, analyzing behavior and deciding the right offer, message, channel, and timing in real time, continuously rather than campaign by campaign. "Agents are collapsing the layers," says @Tasso Argyros, our VP of engineering. @KendraEBarnett covers the full story for @ADWEEK: adweek.com/media/databric…
Good summary of why CustomerLake was a turning point for the industry
Databricks just launched #CustomerLake, and the headline isn't "another CDP." It's the opposite. @databricks' VP of Engineering Tasso Argyros (@tasso) said the quiet part out loud: 👉 "I think the #CDP, as middleware, is going to go away." Not the vendors. The layer itself.
GLM5.2 is the new model from Zai that shocked me today. It scores 81% on Terminal Bench, outperforming Gemini 3.1 Pro. It has open-weights and roughly matches most evals on Opus 4.8 and GPT-5.5. It seems that open-weights models have reached the frontier. What continues to lack behind is fully open-data and open-source models, so that researchers can study the interactions of pre-training, mid-training and post-training and their data.
A PoV: #DataAISummit CMOs will have to include @databricks #CustomerLake in their future CDP evaluations. Partnership with #CDOs is not optional anymore and will be critical for long term value from customer data. databricks.com/blog/introduci…
I won’t lie, presenting in front of 25,000 amazing data & AI professionals is quite the experience. Amazing audience and reception for CustomerLake!
“The agentic CDP needs to be embedded in the lakehouse, it has to be built with LLMs at the core of its architecture, and it has to power infinity campaigns.” Tasso Argyros, VP of Engineering, announces the world's first agentic CDP, CustomerLake.
The @databricks CustomerLake CDP makes a lot of sense, given it is probably the number one use case I have used Databricks for and many others. @tasso did a good job breaking it down in customer360, like 1st & 3rd party ID resolution, & Campaign Agents - really complete vision
@jefrankle i get smarter just by sitting near you Frankle!!
Databricks just announced Databricks CustomerLake, an Agentic CDP natively embedded in the Databricks Lakehouse. "By bringing an AI-native marketing platform directly into the same governed data and AI foundation used by the rest of the business, we equip marketers and data teams with a workforce of agents to deliver the perfect customer experience."
This is a big deal. Millisecond-level analytical queries!! Databricks always won on complex workload performance. This extends its performance leadership across the board, opening up new workloads and use cases for the Databricks Lakehouse!
Databricks Co-founder and Chief Architect @rxin at #DataAISummit, announcing Reyden: a breakthrough new engine for real-time workloads that require immediate responsiveness at high concurrency. “This is the single largest innovation we’ve built since the introduction of
Databricks Data & AI Summit is the worlds largest AI conference! Keynote stage has people as far as the eye can see!
@alighodsi Launch blog: databricks.com/blog/introduci…
After months of intense building, I am super excited to launch CustomerLake today! CustomerLake is Databricks' answer to how Customer Data & Marketing will evolve in the agentic world. AdWeek sat down with @alighodsi and msyelf to dig into the details. Check it out below!
Great system design is 90% finding the right abstractions Great abstractions in retrospect seem obvious Omnigent's core insight is simple and powerful. For most AI tools "the interface to users is the same: messages and files in, text streams and tool calls out" Check it out!
Really excited to open source a new project: Omnigent, a meta-harness for AI agents. It lets you build multi-agent coding and custom agents, sitting above Claude Code, Codex, Pi, and agent SDKs to let you compose them. It also adds live collaboration and rich control policies.
I think what Satya is saying is: 1. Don’t shift all your investment towards AI; keep investing in your human capital 2. Don’t give all your proprietary data to the labs because they’ll eat your lunch; find ways to leverage it in-house 3. if we fail to do (1) and (2), we’ll face a social revolution. I don’t know about 3, but first two make sense and it’s why you need platforms that manage Data + AI together, to create those hybrid learning loops. ps. stay tuned for very relevant announcements from Databricks coming this week!
Spot on. And by the way, lots of relevant announcements coming from Databricks on Tuesday & Wednesday. Stay tuned!
The Fable mess by Anthropic and the previous drama by OpenAI have taught every enterprise buyer one important lesson: don’t put all your eggs in one basket. Humanity will better off because of it.
Check out Omnigent, an open source harness that lets you use all the existing code harnesses (Claude Code, Codex, OpenCode, pi), collaborate and share sessions in many modalities (e.g. Slack/Teams, cli, webui), while having a fine grained security model that really tightens the control on what agents can do/not do. github.com/omnigent-ai/om…
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