Dashboards are effectively dead for many workflows when agents can access applications directly, search for the required tools and complete cross-application tasks.
Claude is an AI assistant developed by Anthropic, positioned as 'The AI for Problem Solvers'. It is a general-purpose AI system used for tasks such as generating code prompts, refining requirements, creating advertising strategy and copy, and processing creative content like storyboarding and video prompts.
Composio is a developer platform for orchestrating just-in-time tool calls, secure delegated authentication, sandboxed execution environments, and parallel execution across integrations with 1,000+ applications. It is designed to let applications and AI agents invoke external tools safely and at scale.
Composio MCP Gateway is a governed MCP gateway for connecting AI agents to Composio's managed tools and customer-provided MCP servers. It provides a centralized endpoint through which agents discover and call tools across services such as Slack, Sentry, Datadog, PostHog, and Metabase, instead of connecting separately to each application. The gateway applies organization, team, user, and action-level access policies; keeps credentials out of agent configuration; manages authentication and token refresh; and records tool calls in a shared audit trail. Composio describes it as supporting more than 1,500 managed integrations and production use by enterprise teams.
Composio Remote Workbench is Composio's agent-oriented remote runtime for multi-step work across connected applications. It lets an agent search for the actions it needs, plan and execute chained tool calls and model calls, and work with code, files, and intermediate results in an isolated sandbox for each execution. Large responses can be processed without loading entire result sets into the agent's context, such as when a generated SQL query uses saved user IDs. Composio also provides delegated authentication and integrations with more than 1,500 applications.
Datadog is a cloud monitoring and observability platform for collecting and analyzing metrics, traces, logs, and related data across infrastructure, applications, networks, containers, databases, and cloud environments. It provides infrastructure monitoring, application performance monitoring, log management, dashboards, alerting, incident management, security monitoring, synthetic monitoring, and user-experience monitoring. The platform is used to identify broken or correlated systems and investigate operational issues, although the video notes that observability alone does not necessarily establish root cause. Datadog also offers AI features, including investigation, remediation, agent observability, and an MCP server.
Metabase is an open-source business intelligence and analytics platform for querying and visualizing data from databases and warehouses. It provides a visual query builder, SQL access, interactive dashboards, reports, alerts, and a governed semantic layer of canonical tables and metrics. Its AI features use the semantic layer to guide natural-language questions and expose the query behind each answer through Metabot or the Metabase MCP server. Metabase also offers APIs, a CLI, Slack connectivity, and an embedding SDK for placing dashboards and self-service analytics in other applications. It can be self-hosted or deployed through Metabase Cloud, and supports permissions, single sign-on, multi-tenant data segregation, caching, configuration export, and embedded analytics.
Model Context Protocol (MCP) is an open, standardized protocol layer for connecting large language models and AI agents with external data sources, hosted infrastructure tools, and other contextual data. It defines formats, metadata, protocol schemas, and APIs for sharing information and tools, attaching, referencing, and validating context such as documents, embeddings, and provenance, while supporting scoped authentication and permissions. MCP translates JSON requests from an agent into calls to service APIs, including CRM, container-management, and GKE capabilities, and can provide design-system context and related tools for generating consistent applications. The official project publishes its specification, documentation, and protocol schema; the schema is defined first in TypeScript and also provided as JSON Schema for broader compatibility. The protocol was created by David Soria Parra and Justin Spahr-Summers, is hosted at modelcontextprotocol.io, and is licensed under the MIT License.
PostHog is an open-source product analytics and developer platform for tracking user events and journeys. It provides web analytics, session replay, feature flags, experiments, error tracking, surveys, data warehousing, and AI observability, and is used to analyze activity such as signups, downloads, posts, captions, credits, and cancellations.
Sentry is a developer-focused application performance monitoring, error-tracking, and debugging platform. It helps identify and trace issues in real-world applications through official SDKs for JavaScript, Python, Ruby, PHP, Go, Rust, Java/Kotlin, C#/F#, C/C++, Dart/Flutter, and other platforms and languages. Its agent-focused features and Sentry MCP expose traces, request and token costs, timelines, chat transcripts, errors, and the full request pipeline so large-language-model agents can investigate and debug production issues. The project is developed by Sentry and is available as an open-source repository with a fair-source topic designation.
Visual Studio Code is a free, open-source Microsoft-developed source-code editor and development environment for Windows, macOS, Linux, and the web, built from the Code - OSS repository. It supports code editing, navigation, code understanding, lightweight debugging, integration with existing development tools, and extensibility through extensions. It provides an environment for building with AI agents that plan, modify, and debug code, managing multi-agent workflows, hosting AI coding plugins such as Claude Code, and running workflows such as Spec Kit commands.
Searchable transcript of Dashboards Are Dead — Sarah Simionescu, Composio — AI Engineer (10:42). Search for a phrase, then click its timestamp to jump straight to that moment in the video.
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00:12 Hello everyone. My name is Sarah and I have a confession to make. So, I've been using Data Dog every day for the last six months. And the other day my co-orker comes up to me and he's like, "Hey Sarah, can I look at this alert?" I'm like, "Okay." So, I open up Data Dog and I'm logged out. It's kind of awkward, so I have to log in and he's like looking over my shoulder and I'm like overly conscious about everything that I'm doing on my computer.
00:40 And I open up the data the data dog dashboard and I just froze. I had no idea where anything was. And this is actually not a knock on data dog. The problem was I had genuinely never opened the dashboard. I've been using it every day without ever looking at it. And so today, I want to make an argument that sounds kind of insane and then show you that it's obvious.
01:04 The dashboard is dead. And I think that's great news for everyone in this room except probably for me because I am my team is responsible for the Composio dashboard. Alas, how did we get here? I believe a postmortem is in order. Shall we? The year is 2022. It is the dark ages. Someone pings me on Slack. They say, "Hey, I get this error when I try and search for prod."
01:34 I would have instantly spawned five different windows. Read the context in Slack. Write a query in data dog. Check post for this session. Uh, fix the bug in VS Code. Open the PR in GitHub. That is five tools and five UIs I have to learn and relearn every time they ship a new redesign. But frankly, redesigns are the least of my problems because every single tool has its own query language.
01:57 So, data dog has its own query syntax. Jira has JQL, search, Slack has its search modifiers. And it gets worse because even when these tools claim to speak the same language, let's say SQL, they don't even agree on SQL. Every dashboard you've ever used, every weird obscure query language ever written was a translation device between you and your data because the machines on the other end could not understand what you actually wanted.
02:25 You never wanted a dashboard or it's cursed query language. You wanted the answer. And in 2023, everything changed. The sparkle button. The sparkle button was born. And with a click of this magical button, an LLM would write a sometimes correct query to get you the answer you need. That is if your question does not require more than two database joins.
02:55 And soon enough, these sparkling buttons were everywhere. So dashboards became AI native. Problem solved, right? Well, not quite. In November of 2024, Enthropic announced the MCP protocol with a promise to create an open standard for connecting AI systems with its data sources and that it did. Instead of using a sparkle button, your own agent, Claude, whom you've already been using every day, could generate the query for you, execute it on your behalf, and give you the answer directly.
03:22 And you might be thinking, problem solved, right? This is the end of the story. Well, far from it, because MCP is a protocol. It's a channel for communication between agents and your service. And it's up to you, the service, to choose how to communicate with the agent. And it turns out that makes all the difference. If you've ever actually wired up a dozen MCP servers, you will know the reality is a mess for three reasons.
03:50 Agents don't learn. Every conversation starts from zero. It has no memory of how it fumbled formatting links properly in Slack yesterday, and so it's just going to fumble again today. And we patched this with skills, but skills is just a band-aid because agents become dumber with the more context you provide. And loading more tools, more skills takes more context.
04:12 And if you connect enough servers, you're dumping thousands of tool definitions straight into the context window. I mean, our GitHub like toolkit alone has over 200 tools. The model just drowns. It grabs the wrong tool or struggles to resolve dependencies and it can't figure out which one it needs to call first. And third, every app is isolated. Each MCP server knows about itself and nothing else.
04:35 So the moment a task spans two apps and like they always do, it is your job to piece that together. And so MCP gave agents a door into every app. But it left them standing in thousands of separate rooms with no map and no memory of ever being there. I'm going to show you a mockup demo here of how I would solve this bug report uh from the very beginning how I would solve it in 2026.
05:03 So imagine in this mockup I have my claude connected to the composio MCP. What I would do is I would literally just copy and paste the link to the message from Slack and say please use century data dog find the root cause a draft PR make no mistakes. And the first thing it does is it calls composio search to state the task it wants to accomplish. So in this case it stated three.
05:26 It wants to fetch slack messages. It wants to search sentry issues. It wants to search data dog logs. And for each of these three, Composeio returns not only the correct tools it needs, but actually a plan of how to use them. So for example, for Slack, you actually have to find the Slack channel ID before you can start quering for messages. Now that it's armed with all the context it needs, it has a plan.
05:48 It starts pulling the message from Slack to see what the user complaint was. Then it begins pulling from data sources in parallel. Data dog sentry and it begins scanning the codebase. Once it identifies the root cause, PR is up with a fix in less than five minutes. I didn't have to build a workflow or write a skill to teach it how to do this. This is literally just claude using Composeio's MCP.
06:12 And it has become so much easier to do things this way that opening up Cloud has just become like muscle memory for me to do literally anything. And the dashboards I used to open every day are becoming increasingly unfamiliar to me. And the impact of designing for agents is measurable. So these are some early unreleased uh results comparing Composio against each app's own native MCP that's listed in the clawed uh marketplace.
06:36 So it's the same tasks, it's the same model, and we see a clear difference. But why? Why is this experience so much better than native MCPs? It's because at Composeio, we are developing a brand new interface specifically designed for agents. We translate messy, sparsely documented, everchanging APIs from all of the apps that you live in every day into something that agents really love to use.
07:04 So, we build on top of MCP CLI and native tools to deliver a cohesive, unified, and excellent experience for your agents that allows them to perform complex operations across these apps seamlessly. I'm going to show you a more classic example of like what I usually use it for every day. So, let's say I want to dig into some user data. I want to see the distribution of what vertical my users select during onboarding.
07:28 I can simply just ask Claude. Claude will again run Composio search. It will write a query in Post Hog, execute it nice and simple. There's my results. But what if I wanted to take this up a notch? So I want to look at that e-commerce section right there. And I want to see what tool kits they like to use. Toolkits is another word for apps we like to use at Composio.
07:54 So let's see if cloud can figure out how it can pull this data from metabase uh given the user ids in post hog. So once again it uses composio search. It queries the user ids of those who selected e-commerce from post hog and it actually saves it without loading the full results into its context. Then it makes some queries uh in metabase to get the database schema sample some data get a feel for how things are structured.
08:21 And once it's confident, it actually uses a very cool tool called Composio Remote Workbench to dynamically generate an SQL query with a reax string to search for all those user IDs again without ever loading the entire thing into its context window. And there we go. So we can see the most popular toolkits amongst this user persona in less than a few minutes.
08:47 Uh and that's data pulled from both sources. If you want to play with the live version, come visit us at the Composio booth. Uh, I'd love to have you play with this live. And so, the dashboard has died. And I'm thinking more and more about what this means for the future. Many startups have begun making their landing pages and their websites AI friendly for GEO purposes.
09:10 But so few have really prepared their applications to be used by agents. Compose began as a tool to help developers. We were taking these popular apps that users like and turn them into these tools that agents could use. But the humans are tired of dashboards and their agents are tired of poorly designed MCP servers. And now we're getting requests from startups saying their clients are begging for a way to use their services through their agents.
09:41 So this is a lesson for everyone building anything right now. You are now serving a new species of user. They don't have eyes. They are not going to click your sparkle button. It shows up with a goal and a set of tools and it judges you on exactly one thing, whether it can get the job done. For a decade, we've built pro uh we've built products to be easy for humans to use.
10:02 And the next era belongs to those that are easy for agents to use. So, picture me again. I'm frozen in front of that data dog dashboard. And I was thinking to myself in that moment that I'm falling behind. Uh, but now I think of that moment as a preview of what's to come. Thank you. [music] >> [music]