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Agent Trace: Capturing the Context Graph of Code We are delighted to collaborate with Cursor, OpenCode, Vercel, Jules, Amp, Cloudflare, and Sasha Varlamov in an open standard for mapping back code:context. here's how we see the potential of code context graphs and the new era of better tooling and... show more

Cognition

180,367 subscribers

39,869 次观看 • 7 个月前 •via X (Twitter)

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13 条评论

Cognition 的头像

Cognition7 个月前

@opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov our full writeup below . Thank you to @leerob for his leadership in driving this spec. We're encouraging the rest of the coding agents industry to join us.

fkz 的头像

fkz7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov Ai slop ppt

Spenser Skates 的头像

Spenser Skates7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov Sign us up:

Rey Neill 的头像

Rey Neill7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov was too ahead of its time

Shamim Hossain 的头像

Shamim Hossain7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov That's great

Cheng Yang 的头像

Cheng Yang7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov without anthropic it will be hard, but nice to see more players try to define new open standards

Kyle Tran 的头像

Kyle Tran7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov Open standard for tracing agent conversations to code is a pragmatic move toward interoperable tooling and tangible ROI

Saïd Aitmbarek 的头像

Saïd Aitmbarek7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov based, let's go!

y_qecea 的头像

y_qecea7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov tahts insane if free

naeem (e/acc) 的头像

naeem (e/acc)7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov Nice, a universally agreed method to store the context of AI generated code.

Robert Hu 🦉 的头像

Robert Hu 🦉7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov Context graphs solve the single biggest failure mode for AI coding agents: they stop asking the codebase what it already knows.

Eddie 的头像

Eddie7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov so sure about wars and usd boosts? history says markets shrug off even the loudest bets. good luck with that crystal ball.

madison 的头像

madison7 个月前

@cursor_ai @opencode @vercel @julesagent @AmpCode @Cloudflare @savarlamov wtf is this music

相关视频

Jira 🤝 Cursor Assign Jira work items to Cursor and let a cloud agent get to work—building, triaging, and opening PRs with context from the Atlassian Teamwork Graph. One connected workflow for teams and AI agents, from intent to code. Try it now:

Jira 🤝 Cursor Assign Jira work items to Cursor and let a cloud agent get to work—building, triaging, and opening PRs with context from the Atlassian Teamwork Graph. One connected workflow for teams and AI agents, from intent to code. Try it now:

Atlassian

21,607 次观看 • 3 个月前

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

Jurij Skornik

11,180 次观看 • 3 个月前

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf (retired), VSCode + GitHub Copilot Chat, and Cline.

Context is power. Context shared is power multiplied. Every AI agent today builds context, then loses it. Work doesn't compound. OriginTrail is built differently: three memory layers, one shared graph, provenance baked in. DKG v10 now integrates with Hermes of Nous Research, OpenClaw🦞, Claude (Code & Desktop), Cursor, Codex by OpenAI, Windsurf (retired), VSCode + GitHub Copilot Chat, and Cline.

OriginTrail

1,723,329 次观看 • 4 个月前

Google Engineers just showed how their AI engineers are moving from simple "RAG" to "graphs" RAG → Graph RAG → Memory → Multimodal Agent Graphs. 0% → 15:32 - setting up the production agent stack 30% → 41:00 - Graph RAG with semantic 65% → 58:00 - extracting graph context 100% → 1:09:00 - orchestrating specialized agents 90-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Google Engineers just showed how their AI engineers are moving from simple "RAG" to "graphs" RAG → Graph RAG → Memory → Multimodal Agent Graphs. 0% → 15:32 - setting up the production agent stack 30% → 41:00 - Graph RAG with semantic 65% → 58:00 - extracting graph context 100% → 1:09:00 - orchestrating specialized agents 90-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Morty

52,561 次观看 • 15 天前

Cognition has signed a definitive agreement to acquire Windsurf. The acquisition includes Windsurf’s IP, product, trademark and brand, and strong business. Above all, it includes Windsurf’s world-class people, whom we’re privileged to welcome to our team. We are also honoring their talent and hard work in building Windsurf into the great business it is today. This transaction is structured so that 100% of Windsurf employees will participate financially. They will also have all vesting cliffs waived and will receive fully accelerated vesting for their work to date. At Cognition we have focused on developing robust and secure autonomous agents, while Windsurf has pioneered the agentic IDE. Devin + Windsurf are a powerful combination for the developers we serve. Working side by side, we’ll soon enable you to plan tasks in an IDE powered by Devin’s codebase understanding, delegate chunks of work to multiple Devins in parallel, complete the highest-leverage parts yourself with the help of autocomplete, and stitch it all back together in the same IDE. Cognition and Windsurf are united behind a shared vision for the future of software engineering, and there’s never been a better time to build. Welcome to our new colleagues from Windsurf!

Cognition

3,052,668 次观看 • 1 年前

Google Engineers just showed how their AI engineers are building Context Graphs from 0% to 100%: 10% → 4:01 - how to build a Context Graph 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs 54-minute Google workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full Context Graphs Playbook below.

Google Engineers just showed how their AI engineers are building Context Graphs from 0% to 100%: 10% → 4:01 - how to build a Context Graph 30% → 15:00 - Graph Engineering explanation 55% → 30:00 - Agentic search Engineering 80% → 35:48 - Graph Engineering practice 100% → 47:06 - self-improving agents in graphs 54-minute Google workshop, and it’s one of the clearest hands-on examples of the shift from. Watch it today, then read the full Context Graphs Playbook below.

Morty

10,451 次观看 • 11 天前

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Knowledge graphs are infinitely better than vector search for building the memory of AI agents. With five lines of code, you can build a knowledge graph with your data. When you see the results, you'll never go back to vector-mediocrity-land. Here is a quick video:

Santiago

398,277 次观看 • 1 年前

I built a form-filling agent that anyone can use 💫 This is an extremely simple but useful (I hope) app. Upload a fillable form 📋, some context files, and chat with the agent to fill the form out automatically ✍️ 1️⃣ Yes it is a Claude Code SDK wrapper 2️⃣ It is better and faster than ChatGPT/Claude UI out of the box 3️⃣ We use LlamaParse to parse the context files, so you can have more trust that we are able to read context without hallucinations (e.g. messy scanned handwriting, drivers license photo, and more). This was one of my holiday Claude Code vibe-coding projects. Built with Opus 4.5, and also powered by Opus 4.5. Feeling the AGI 🫡 App is here: Repo is open-source:

I built a form-filling agent that anyone can use 💫 This is an extremely simple but useful (I hope) app. Upload a fillable form 📋, some context files, and chat with the agent to fill the form out automatically ✍️ 1️⃣ Yes it is a Claude Code SDK wrapper 2️⃣ It is better and faster than ChatGPT/Claude UI out of the box 3️⃣ We use LlamaParse to parse the context files, so you can have more trust that we are able to read context without hallucinations (e.g. messy scanned handwriting, drivers license photo, and more). This was one of my holiday Claude Code vibe-coding projects. Built with Opus 4.5, and also powered by Opus 4.5. Feeling the AGI 🫡 App is here: Repo is open-source:

Jerry Liu

48,450 次观看 • 8 个月前

Introducing Qwen 3 + Deep Research! We built a deep research agent with Qwen 3 and it performed better than most open source models that we've tried. the code is completely open source and free! built with 's Langgraph Together AI and uses Perplexity Tavily for search here's the code link:

Introducing Qwen 3 + Deep Research! We built a deep research agent with Qwen 3 and it performed better than most open source models that we've tried. the code is completely open source and free! built with 's Langgraph Together AI and uses Perplexity Tavily for search here's the code link:

Soham

34,566 次观看 • 1 年前

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Google just showed how AI engineers are moving from simple "RAG" to "graphs", memory and multimodal agents • 15:32 - setting up the production agent stack • 28:00 - turning disconnected data into a knowledge graph • 41:00 - Graph RAG with semantic + hybrid search • 58:00 - extracting graph context from images, text and video • 1:09:00 - orchestrating specialized agents with ADK • 1:20:00 - giving agents persistent memory across sessions 94-minute Google Cloud workshop, and it’s one of the clearest hands-on examples of the shift from -> RAG → Graph RAG → Memory → Multimodal Agent Graphs. The workshop uses Spanner Graph, Gemini, ADK and Memory Bank to build that stack. Watch it today, then read the full “From RAG to Context Graphs” roadmap below.

Morlex

74,134 次观看 • 1 个月前

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Nikos

19,466 次观看 • 26 天前

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,435 次观看 • 2 年前

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

155,647 次观看 • 1 年前

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

Mike Fishbein

10,171 次观看 • 3 个月前

AI agents work better when they bring in context step by step. With Box Agent skills and progressive disclosure of LangChain skills, developers can quickly build AI agents that load the right skill at the right time, stay grounded in Box content, and keep access tightly scoped. Less context bloat. More control. Faster enterprise AI development.

AI agents work better when they bring in context step by step. With Box Agent skills and progressive disclosure of LangChain skills, developers can quickly build AI agents that load the right skill at the right time, stay grounded in Box content, and keep access tightly scoped. Less context bloat. More control. Faster enterprise AI development.

Box

627,810 次观看 • 4 个月前

A new era of AI is forming, with autonomous AI agents at the forefront. We are harnessing this trend together with our recently announced new launch format - Seedify Open Sales - and the combination is explosive 💣 Meet AgentsAI - the launch of their token $AGENT is coming soon in an Open Sale AND an IDO! This is a sale you don't wanna miss: $1M FDV and Fully Unlocked! AgentsAI is propelling the digital evolution with their multiple services that unlock the full potential of autonomous AI agents. Take a closer look: 🦾Merge of AI technology with decentralized platforms 🦾Create and launch AI Agent tokens in less than 60 seconds, and under $1 🦾Complete marketplace of AI agents and other AI tools The Open Sale and IDO start on November 11th and registrations are now open so hurry!

A new era of AI is forming, with autonomous AI agents at the forefront. We are harnessing this trend together with our recently announced new launch format - Seedify Open Sales - and the combination is explosive 💣 Meet AgentsAI - the launch of their token $AGENT is coming soon in an Open Sale AND an IDO! This is a sale you don't wanna miss: $1M FDV and Fully Unlocked! AgentsAI is propelling the digital evolution with their multiple services that unlock the full potential of autonomous AI agents. Take a closer look: 🦾Merge of AI technology with decentralized platforms 🦾Create and launch AI Agent tokens in less than 60 seconds, and under $1 🦾Complete marketplace of AI agents and other AI tools The Open Sale and IDO start on November 11th and registrations are now open so hurry!

vibe/vibe

40,733 次观看 • 1 年前

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Introducing Claude Code Hook - Context Timeline (Saving this to try later) Install with: npx claude-code-templates@latest --hook monitoring/context-timeline Managing the context window and the subagents running in Claude Code is hard to keep track of That's why I built this hook... It starts the moment you open a session and shows a timeline with the main agent's context window and how subagents start working in their own separate context Every subagent you have running will show up in real time This way you can manage the context and the subagents you run, and see everything in a much simpler way than in the console

Daniel San

51,706 次观看 • 4 个月前

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

AI agents are becoming increasingly capable of taking actions beyond the chat window and interacting with tools in the real world, such as web browsers. We explored this capability by having an AI agent present one of our interactive web-based demos. In this video, it's presenting our election interference demo. This agent is a Cursor coding agent and is equipped with tools to read and interact with the browser (using Playwright) and perform text-to-speech (with ElevenLabs), in addition to the standard Cursor tools.

CivAI

18,428 次观看 • 9 个月前