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A few talks at the intersection of single cell biology x agent engineering: - Understanding library prep + sequencing is important - Repurposing data infrastructure for agent consumption - How to benchmark frontier agents on single cell analysis

Kenny Workman

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19,047 просмотров • 2 месяцев назад •via X (Twitter)

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Today, we’re introducing Decide Agent, a frontier-level AI agent for Excel analysis. Decide Agent scored 82.50% on SpreadsheetBench, the public standard benchmark for Excel AI agents, ranking #4 globally. This is the same leaderboard used by Claude, OpenAI, and Microsoft to evaluate their spreadsheet agents. We’re here to push the frontier in Excel and data analytics. Decide Agent is now generally available at trydecide . ai

Today, we’re introducing Decide Agent, a frontier-level AI agent for Excel analysis. Decide Agent scored 82.50% on SpreadsheetBench, the public standard benchmark for Excel AI agents, ranking #4 globally. This is the same leaderboard used by Claude, OpenAI, and Microsoft to evaluate their spreadsheet agents. We’re here to push the frontier in Excel and data analytics. Decide Agent is now generally available at trydecide . ai

Ab.

40,512 просмотров • 6 месяцев назад

We made an explainer on the power of single cell technology to help understand biology and disease at a single cell level. Thanks Illumina for production support. Clip below and full video at:

We made an explainer on the power of single cell technology to help understand biology and disease at a single cell level. Thanks Illumina for production support. Clip below and full video at:

OzSingleCell Omics

19,797 просмотров • 3 лет назад

Gave a talk to Machine Learning @ Berkeley on benchmarking frontier models on spatial biology. Why understanding how assays work is important, what verifiability might look like with messy biology + infrastructure challenges running agentic evals at scale.

Gave a talk to Machine Learning @ Berkeley on benchmarking frontier models on spatial biology. Why understanding how assays work is important, what verifiability might look like with messy biology + infrastructure challenges running agentic evals at scale.

Kenny Workman

15,784 просмотров • 3 месяцев назад

CHOIR is now online Nature Genetics! CHOIR is a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR scales to millions of cells and works with single-/multi-omic data of any type!

CHOIR is now online Nature Genetics! CHOIR is a new clustering method for single-cell data that evaluates whether clusters represent statistically distinct cell populations. CHOIR scales to millions of cells and works with single-/multi-omic data of any type!

Cathrine Sant

55,294 просмотров • 1 год назад

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

🔬 Exciting News! Our manuscript, "scGPT: toward building a foundation model for single-cell multi-omics using generative AI" is now finally published in Nature Methods (Nature Methods) 🎉 !!! (Re-)Introducing scGPT: A transformative foundation model engineered for single-cell omics analysis. Developed through the analysis of over 33 million human cells, scGPT sets a new benchmark for application versatility, offering both fine-tuning and zero-shot capabilities. Since its preprint in May 2023, scGPT has significantly impacted the field, evidenced by 13K+ installations, 600+ GitHub stars 🌟, and 40+ citations before its official publication! scGPT has been validated by numerous benchmark studies as a leading foundation model in single-cell analysis. Its pre-trained embeddings extend its utility beyond single-cell studies, enhancing a variety of downstream tasks including protein enrichment and genetic perturbation predictions. Some key updates lately: ---Expanded zero-shot applications for efficient reference mapping and integration, now with CellXGene census integration. ---Advanced perturbation analysis capabilities, including genome-scale perturb-seq data analysis and bulk sequencing data generalization. ---Upgraded scGPT package, offering versatile model loading compatible with PyTorch and flash-attn, for both GPU and CPU. ---Cloud-based scGPT applications for reference mapping, cell annotation, and gene regulatory network inference are available on ---Integration with Hugging Face for easier model training. Limitations: scGPT is an early foray into foundation models for single-cell omics, facing challenges like limited zero-shot learning in some tasks, pretraining constraints, data quality issues, and evaluation limitations. See our Supplementary Notes for details. 🚀 Future Work? Short-Term Goals: 1. Releasing a Mouse Model for broader analysis. 2. Developing a comprehensive evaluation suite for foundation models in single-cell analysis. 3. Creating a foundation model for single-cell spatial omics. 4. Enhancing zero-shot capacity by integrating scGPT with RAG (e.g., knowledge graphs). Long-Term Goals: 1. Expanding scGPT for comprehensive single-cell multi-omics analysis. 2. Developing an in-silico perturbation model for predicting genetic perturbation effects. 3. Merging scGPT with multi-modal genomic sequence models for a deeper understanding of cell biology. 📚 Access the paper on Nature Methods: 🔬Preprint in Bioarixv: 💻 All our codes/data/weights are open source: Wholehearted congratulations to all the authors, especially the two co-first authors, Haotian (Haotian Cui ) and Chloe (ChloeXWang), who are really the emerging superstars in AI and biology! Vector Institute Peter Munk Cardiac Centre AI U of T Department of Computer Science Department of Laboratory Medicine & Pathobiology University Health Network University of Toronto #scGPT #GenerativeAI #AI4Science #Combio #opensource

Bo Wang

199,747 просмотров • 2 лет назад

Biology is by far the most advanced technology on the planet, self-assembling nanobots that can unpack a single cell into a space-faring civilization in a few billion years.

Biology is by far the most advanced technology on the planet, self-assembling nanobots that can unpack a single cell into a space-faring civilization in a few billion years.

Andrew Côté

323,390 просмотров • 1 год назад

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to 100 agents running inside one graph: 17:44 - Build your first AI agent 39:30 - Run agents with loop engineering 1:12:38 - Turn agent loops into graphs 1:34:26 - Build agents that throttle themselves 1:55:05 - Orchestrate the full multi-agent system Most people build one agent and stop there. Google is teaching the full stack: Prompt → Agents → Loops → Graphs → Multi-Agent Systems Single agents are the old workflow. Self-regulating agent graphs are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read the full architecture below ↓

Lunar

399,182 просмотров • 18 дней назад

We’ve developed a new way to mix capabilities from specialized AI agents into a single collective output, starting with research analytics. With Butler, you can combine your favorite research agents into one coherent analysis, all triggered by a single prompt. This marks a major step toward agent-to-agent collaboration becoming the new default for research.

We’ve developed a new way to mix capabilities from specialized AI agents into a single collective output, starting with research analytics. With Butler, you can combine your favorite research agents into one coherent analysis, all triggered by a single prompt. This marks a major step toward agent-to-agent collaboration becoming the new default for research.

Virtuals Protocol

53,733 просмотров • 9 месяцев назад

A talk on verifiable environments for agents in biology 0:00 Where does experimental data come from? 1:41 Modern bio research organized around measurement 2:44 Data analysis is an executable substrate for science 3:23 Five years of learnings building products for pharma 4:13 Trying to use coding models for biology workflows 5:40 Why frontier models can't be trusted (yet) 6:44 Sequencing based spatial analysis 7:38 SpatialBench design principles 8:26 Anatomy of a single evaluation 9:40 Human verification and early long horizon extensions 13:12 Grading functions and the case against rubrics 14:07 New work in multi-omics, therapeutics, biosecurity

A talk on verifiable environments for agents in biology 0:00 Where does experimental data come from? 1:41 Modern bio research organized around measurement 2:44 Data analysis is an executable substrate for science 3:23 Five years of learnings building products for pharma 4:13 Trying to use coding models for biology workflows 5:40 Why frontier models can't be trusted (yet) 6:44 Sequencing based spatial analysis 7:38 SpatialBench design principles 8:26 Anatomy of a single evaluation 9:40 Human verification and early long horizon extensions 13:12 Grading functions and the case against rubrics 14:07 New work in multi-omics, therapeutics, biosecurity

Kenny Workman

89,760 просмотров • 23 дней назад

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:

PantheonOS allows any biologist to perform complex data analyses of emerging single cell, multi-omics and spatial transcriptomics datasets end to end through AI agent and human collaboration. We are releasing six replays of use cases trajectories. Each "trajectory" is a complete end-to-end run, from prompt to analysis, to figures, and finally to report, and in fully inspectable and reproducible manner. See the six user cases in our PantheonOS Gallery: 1. 3D mouse embryo analysis: Tangram deconvolution and PyVista-based 3D visualization of E6 mouse embryo spatial transcriptomics data 2. 3D human fetal heart analysis: Spatial mapping of heart disease gene patterns using MERFISH data 3. Multi-omics spatial integration: Single cell multi-omics-to-spatial mapping with MOSCOT optimal transport 4. Gene panel design: 1000-plex immune-oncology MERFISH gene panel optimization 5. Cell segmentation benchmarking: Comparative evaluation of Cellpose-SAM, InstanSeg, StarDist, and other tools 6. Spatial disease biology: Ligand–receptor analysis of disease-associated tissue microenvironments We would love to hear how you can use PantheonOS for your research! Got an interesting agent run of your own and want to share? In the Pantheon UI, click Export Bundle (top-right of any chat) to package the full trajectory — chat history, code, figures, report — then submit it here:

evo-devo

13,393 просмотров • 3 месяцев назад

Google just dropped a free 8-minute lesson on building your first AI agent. This is the clearest starting point for understanding how AI agents actually work. People are paying $500 for courses that teach less than this. Watch it to build your first agent, then read the step-by-step guide on graph engineering below to run tens of them at once.

Google just dropped a free 8-minute lesson on building your first AI agent. This is the clearest starting point for understanding how AI agents actually work. People are paying $500 for courses that teach less than this. Watch it to build your first agent, then read the step-by-step guide on graph engineering below to run tens of them at once.

Anatoli Kopadze

106,182 просмотров • 29 дней назад

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that builds itself: 10:16 - Build your first AI agent 41:05 - Master prompt engineering 54:45 - Turn agents into graphs 1:20:10 - Run loops inside agent graphs 1:43:33 - Build a graph that builds itself Most people ship one agent and stop there. Google is teaching everything after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read how to run 1,000 agents from one prompt below ↓

Google just released a free 2-hour course on full Graph Engineering. How to go from one prompt to an agent graph that builds itself: 10:16 - Build your first AI agent 41:05 - Master prompt engineering 54:45 - Turn agents into graphs 1:20:10 - Run loops inside agent graphs 1:43:33 - Build a graph that builds itself Most people ship one agent and stop there. Google is teaching everything after: Prompt → Agents → Graphs → Loops → Self-Building Systems Single agents are the old workflow. Graphs that evolve themselves are the new one. This 2-hour watch is worth more than most paid agent engineering courses. Bookmark and watch it today Then read how to run 1,000 agents from one prompt below ↓

Lunar

49,332 просмотров • 17 дней назад

Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown

Google dropped a free 2 hour course on full graph engineering, taking you from a single prompt to a hundred agents wired into loops and graphs 17:44 – Building your first agent 39:30 – Loop engineering, iterate, check, break 1:12:38 – Graph engineering 1:34:26 – Agents that throttle themselves 1:55:05 – Full graph for multi agentic systems Most people build a single agent and stop there. This course shows the full system, where agents wire themselves into a graph on their own Watch it today, then check out the graph engineering playbook below for a deeper breakdown

Wayne

140,885 просмотров • 10 дней назад

Placing trades with our no-code Polymarket agent is giving me butterflies. → Live data piped from our Dome agents → An agent then runs statistical analysis on each market → The agent then autonomously decides which bet to make, and places it for me. Zero. code.

Placing trades with our no-code Polymarket agent is giving me butterflies. → Live data piped from our Dome agents → An agent then runs statistical analysis on each market → The agent then autonomously decides which bet to make, and places it for me. Zero. code.

Tim

29,304 просмотров • 10 месяцев назад

1/ The biggest blocker to effective AI-in-bio is lack of quality data, but Vevo Therapeutics is open-sourcing their gigantic #Tahoe100M single cell dataset (on Arc Institute's new Virtual Cell Atlas)! I talk w/Vevo's founders and Arc CEO/CTO about "Virtual Cell Models" and significance

1/ The biggest blocker to effective AI-in-bio is lack of quality data, but Vevo Therapeutics is open-sourcing their gigantic #Tahoe100M single cell dataset (on Arc Institute's new Virtual Cell Atlas)! I talk w/Vevo's founders and Arc CEO/CTO about "Virtual Cell Models" and significance

sarah guo

44,512 просмотров • 1 год назад

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:

🚀 Introducing PantheonOS ( A Fully Open-Source Agent OS for Science PantheonOS began as a research project in my Stanford lab and has since evolved into a vision to redefine data science in the era of AI—starting with computational biology, especially single-cell and spatial genomics. PantheonOS is a general agent platform built from the ground up. It is arguably the first distributed agent framework designed for scientific data analysis. 🔑 Key Features 1. Multi-Agent Collaboration – Built-in paradigms for distributed, cross-machine cooperation among agents and toolsets. 2. Native Toolset Support – Python, R, Julia, LaTeX, and more—designed for real scientific workflows. 3. Modular & Extensible – Developer-friendly design with shallow wrappers, plus LLM-driven toolset generation. 4. Evolvable Agents – Capable of evolving large-scale code projects to achieve superhuman performance (e.g., evolving upon the original Harmony [I Korsunsky, 2019, Nature Biotechnology] and Scanorama [BL Hie, 2019, Nature Biotechnology] implementations), and even evolving the system itself to adapt to new fields. 🎉 Stepwise Release Strategy We’re releasing PantheonOS in stages: Pantheon-CLI (today!), followed by Pantheon-Lab, Pantheon-Notebook, Pantheon-Slack, and more. 🌟 Pantheon-CLI Highlights - We're not just building another CLI tool. We're defining how scientists will interact with data in the AI era. - Open, Powerful, Python-First – The first fully open-source, endlessly extendable scientific “vibe analysis” framework. - Mixed Programming Magic – Combine Python, natural language, R, or Julia—seamlessly in the same environment. - PhD-Level Assistant – A command-line agent for complex real-world genomics and beyond, handling workflows at the PhD level. - Privacy by Design – Run entirely offline with local LLMs—your data never leaves your computer. ✅ Proven Applications (10 Demonstrations) Computational biology: 1. ATAC-seq: From raw reads to peak matrix 2. RNA-seq: From raw reads to expression matrix 3. Complex single-cell workflows (PhD-level) 4. Hybrid natural language + R for Seurat annotation 5. Learning from web tutorials + invoking single-cell foundation models 6. Cell segmentation on 10x Genomics HD Visium data And beyond: 7. Mixed Python & R programming examples 8. Molecular docking & structural analysis 9. Exploratory factor analysis for behavioral survey data 10. Customer segmentation & finance analytics 🌐 Learn More & Get Started Website: Pantheon-CLI Documentation: GitHub Repo: 💬 Join our community: PantheonOS Slack: PantheonOS Discord:

evo-devo

17,410 просмотров • 1 год назад

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

Google just dropped a free 1-hour course On building complete agent systems from scratch: 07:02 - Build your first AI agent 13:34 - Give every agent a specialized skill 23:08 - Engineer the right context 32:56 - Connect agents into graphs 54:11 - Run 1,000 agents in autonomous loops One agent saves you an hour A thousand agents replace the team you cannot afford to hire Most people are still learning how to prompt one chatbot Google is already teaching how to build entire agent systems Agent → Skills → Context → Graphs → Loops Single agents are the old workflow Agent swarms are the new one This free 1-hour course is worth more than most paid agent engineering programs Bookmark it before everyone starts building the same thing Then build your first agent with the guide below

rari

28,241 просмотров • 23 дней назад

When agents start opening PRs, code review has to change. Join the free Background Agents Summit for practical talks on agent review, risk, audit trails, and how engineering teams can adopt background agents without losing trust. Register for May 6-7.

When agents start opening PRs, code review has to change. Join the free Background Agents Summit for practical talks on agent review, risk, audit trails, and how engineering teams can adopt background agents without losing trust. Register for May 6-7.

Ona

1,220,198 просмотров • 3 месяцев назад

Agent swarm above single agents? The more data input to one agent, the more risk of hallucination. INFINIT's specialized agent swarms prevent this, each specialized only in their domain, coordinating like a high-performing team. Less confusion, more precision. Listen to Tascha (@Infinit_Labs) discuss in our podcast with blocmates.

Agent swarm above single agents? The more data input to one agent, the more risk of hallucination. INFINIT's specialized agent swarms prevent this, each specialized only in their domain, coordinating like a high-performing team. Less confusion, more precision. Listen to Tascha (@Infinit_Labs) discuss in our podcast with blocmates.

INFINIT

77,088 просмотров • 9 месяцев назад

Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

Google just released a free 2-hour+ course on full agent engineering. How to go from one prompt to a system that runs while you sleep: 38:46 - Build your first AI agent 54:46 - Connect MCP tools 1:12:43 - Run agents with four loop patterns 1:20:57 - Turn agent loops into graphs 2:22:31 - Build the complete autonomous system Most people are still building one agent and stopping there. Google is already teaching the full stack: Agents → Tools → Loops → Graphs → Autonomous Systems Single agents are the old workflow. Systems that run without you are the new one. This course is worth more than most paid agent engineering bootcamps. Bookmark and watch it today Then read the full graph engineering playbook below

Lunar

196,723 просмотров • 25 дней назад