Everywhere Agents: AI that meets you where you work

Aug 4

The Note-Taking Trap

We're living through the golden age of AI-powered note-taking apps. Notion's AI writes summaries. Obsidian connects ideas automatically. Otter transcribes meetings with superhuman accuracy. Roam builds knowledge graphs from scattered thoughts.

These tools are genuinely impressive. They make it easier than ever to capture, organize, and retrieve information. But they all share the same fundamental limitation: they're productivity cul-de-sacs.

Here's what I mean. You have a productive meeting with your product team. Your AI note-taking app dutifully transcribes everything, identifies action items, and even generates a clean summary. Beautiful. But then what?

You still have to:

Copy the bug reports into Jira tickets
Paste the feature requirements into your product spec
Send the timeline updates to Slack
Add the action items to Asana
Update the project status in Monday.com
Email the client summary to stakeholders

Your AI did the easy part—capturing and organizing information. You're still stuck with the tedious part—moving that information to where it actually needs to live and work.

This is the app-switching tax, and it's killing our productivity. Not because any individual tool is bad, but because our workflows span multiple tools, while our AI assistants are trapped in single apps.

The Workflow Reality

Let's be honest about how knowledge work actually happens in 2025.

Your marketing brief doesn't live in your note-taking app—it lives in Google Docs where your team can collaborate on it. Your bug reports don't belong in your meeting notes—they belong in Jira where engineers can prioritize and fix them. Your client updates don't stay in your AI assistant—they become Slack messages, email threads, and project dashboard updates.

The most useful information is information in motion. It's data that flows from capture to action, from idea to implementation, from individual insight to team coordination.

But today's AI productivity tools treat information like static artifacts. They help you create better notes, but they don't help those notes become better work.

This is backwards. The future of AI productivity isn't about building the perfect note-taking app. It's about building AI that understands your entire workflow and can act across all your tools.

Introducing Everywhere Agents

Imagine an AI assistant that doesn't just capture your thoughts—it understands what those thoughts need to become and makes it happen automatically.

You're in a client meeting, and you mention that the login flow is confusing users. Instead of creating another note that you'll forget to act on, your AI agent:

Recognizes this as a UX issue that needs tracking
Creates a properly formatted Jira ticket with relevant context
Assigns it to the UX team based on your team structure
Adds it to the current sprint if it's high priority
Updates your client dashboard to show issue acknowledgment
Schedules a follow-up task to check resolution status

All of this happens in the background while you continue your conversation. No app switching. No copy-pasting. No forgotten action items.

This is what I call an Everywhere Agent—AI that meets you where you work, understands what you're trying to accomplish, and takes action across your entire tool ecosystem.

How Everywhere Agents Work

The technology stack for Everywhere Agents builds on three core capabilities that are finally mature enough to make this vision practical:

1. Context Detection

Modern language models have gotten remarkably good at understanding intent and categorizing content. They can reliably distinguish between:

Product requirements that need to become feature specs
Bug reports that need to become Jira tickets
Marketing ideas that need to become campaign briefs
Meeting action items that need to become calendar events
Client feedback that needs to become customer support tickets
Team updates that need to become Slack messages

But context detection goes deeper than content classification. The AI also understands:

Urgency indicators: "This is blocking the launch" vs "nice to have for v2"
Ownership patterns: Which team members typically handle which types of work
Workflow stages: Whether something is an initial draft, needs review, or is ready for implementation
Relationship mapping: How different pieces of information connect to existing projects and priorities

The key breakthrough is that LLMs can now perform this contextual analysis with enough reliability to automate routing decisions. You don't need perfect accuracy—you need good enough accuracy with easy correction mechanisms.

2. Automatic Routing

Once the AI understands what information it's dealing with, it can automatically route that information to the appropriate destination:

Jira Integration: Product bugs become properly formatted tickets with appropriate labels, components, and priority levels. The AI includes enough context for engineering teams to understand and prioritize the issue without additional meetings.

Document Creation: Marketing ideas become Google Doc drafts with proper templates and sharing permissions. Product requirements become structured specs in Confluence with all the necessary sections pre-populated.

Task Management: Action items become tasks in Asana, Monday.com, or your team's preferred project management tool. The AI sets due dates based on urgency indicators and assigns ownership based on team structure.

Communication Routing: Team updates become Slack messages in the right channels. Client communications become email drafts with proper formatting and recipients. Status updates become dashboard entries that stakeholders can access.

Calendar Integration: Scheduling requests become calendar invites with proper attendees and agenda items. Follow-up reminders become calendar blocks with relevant context and preparation materials.

The routing isn't just about moving information—it's about transforming it into the format and structure that works best for each destination tool.

3. Two-Way Synchronization

This is where Everywhere Agents become truly powerful. They don't just push information out—they monitor changes across your entire tool ecosystem and keep everything synchronized.

When someone updates a Jira ticket status, your central knowledge base reflects that change. When a client responds to an email, the relevant project dashboard gets updated. When a deadline shifts in Asana, related calendar items automatically adjust.

This creates a single source of truth that actually stays true, because it's constantly synchronized with all the distributed sources where real work happens.

The synchronization also enables intelligent notifications. Instead of getting bombarded with updates from every tool, your AI agent filters and prioritizes changes that actually matter to you. It knows the difference between routine progress updates and critical blockers that need your immediate attention.

Agent Chaining: Workflow Automation

The real magic happens when you combine context detection, automatic routing, and two-way sync into agent chains—sequences of automated actions that handle entire workflow processes.

Consider a typical product team workflow:

Input: "Our conversion rate dropped 15% after the checkout redesign"

Agent Chain:

Analysis Agent recognizes this as a critical product issue
Ticket Agent creates a high-priority Jira ticket for investigation
Communication Agent posts an alert in the #product-critical Slack channel
Research Agent pulls relevant analytics data and attaches it to the ticket
Scheduling Agent sets up an emergency triage meeting with key stakeholders
Documentation Agent creates a incident response document template
Monitoring Agent sets up automated alerts for further conversion rate changes

This entire workflow happens automatically based on a single input. No manual coordination, no forgotten steps, no delayed responses.

Or consider a marketing campaign workflow:

Input: "We should create a case study about the DataCorp implementation"

Agent Chain:

Planning Agent creates a content brief in the marketing folder
Assignment Agent identifies the best writer based on technical expertise and current workload
Research Agent gathers relevant customer data, project outcomes, and quotes
Coordination Agent schedules an interview with the customer success manager
Timeline Agent adds the case study to the content calendar with realistic deadlines
Review Agent sets up approval workflows with legal and customer success teams
Distribution Agent prepares social media and email campaign templates for post-publication

Each agent in the chain adds value while maintaining context about the overall goal.

The Business Impact

Everywhere Agents aren't just about individual productivity—they're about organizational intelligence. When AI can automatically route, format, and track information across your entire tool stack, several powerful things happen:

Reduced Context Switching

Knowledge workers switch between apps an average of 1,100 times per day. Each switch costs cognitive overhead and breaks flow state. Everywhere Agents reduce app switching by eliminating the manual work of moving information between tools.

Instead of "capture in notes app → remember to create ticket → open Jira → format information → assign and prioritize," the workflow becomes "capture anywhere → AI handles the rest." This isn't just faster—it removes the cognitive burden of remembering and executing multi-step workflows.

Improved Team Coordination

When information automatically flows to the right places in the right formats, teams stay aligned without coordination overhead. Product managers don't need to chase developers for bug reports. Marketing teams don't need to hunt through meeting notes for campaign ideas. Client feedback automatically reaches the people who can act on it.

The AI becomes a coordination layer that connects different team workflows without requiring everyone to use the same tools or follow the same processes.

Better Decision Making

Everywhere Agents create organizational memory that persists across tool boundaries. When someone makes a decision in Slack, that context is preserved in the relevant project documentation. When a client raises a concern in email, it's automatically connected to the product roadmap discussion.

This connected information enables better decisions because people have access to relevant context without having to manually hunt for it across multiple tools and conversations.

Reduced Information Silos

Traditional productivity tools create information silos. Marketing ideas live in marketing tools, engineering discussions live in engineering tools, and executive decisions live in executive tools. Everywhere Agents break down these silos by automatically sharing relevant information across team boundaries.

A customer complaint in Zendesk can automatically create a product requirement in Jira and a marketing response task in Asana. The information flows to where it's needed without manual coordination or political negotiations about which tool everyone should use.

Real-World Implementation Scenarios

Let's walk through how Everywhere Agents would work in practice across different types of organizations:

Software Development Teams

Daily Standup Scenario: During your standup, you mention "The user authentication is taking too long to load on mobile devices."

Traditional Workflow: Someone remembers to create a ticket after the meeting. Maybe. If they remember the details correctly. And if they have time between other priorities.