Capture the context your AI agents need
FusedFrames is a desktop app that records what your team does and why, then makes it queryable by your AI agents so they operate with the same level of judgement.
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How it works
From keystroke to queryable knowledge.
Records every action
Runs in the background on your team's desktops, observing every action across every app they use.
Interprets in context
AI interprets the action in real time and understands how it relates to other actions.
Captures the reasoning
Asks for context when AI detects a decision that couldn't be inferred from the action alone.
Builds queryable patterns
Distills actions and reasoning into structured patterns with a trigger, sequence and outcome.
Serves your AI agents
Serves the patterns to your AI agents over API or CLI, ready for them to query before they act.
What your AI agents get
Things FusedFrames feeds your runtime.
API and CLI access
Exposes everything over API and CLI, ready for your AI agents to query before they take action.
{
"id": "pat_abc123",
"title": "Triage incoming bug report from customer",
"behaviour": "When a customer reports broken or unexpected behaviour, the support team checks the affected component for known incidents, searches the customer's account for matching errors, and then either sends a workaround, walks the customer through a config fix, or files a bug in Linear and tags the on-call engineer.",
"reasoning": "Most reported bugs are config issues or known issues with workarounds, so the support team handles them directly to keep engineering focused on real defects. Enterprise customers get a Linear ticket regardless of how the case is resolved, so engineering always has visibility on issues affecting them.",
"trigger": "A ticket arrives in Zendesk describing broken or unexpected behaviour in the product.",
"outcome": "The customer gets the right next step — a workaround, a config fix, or a logged bug owned by engineering — and engineering has visibility on every issue affecting an enterprise account.",
"category": "Customer Support",
"tags": [
"bug-triage",
"escalation"
],
"applications": [
"Zendesk",
"Statuspage",
"Sentry",
"Linear",
"Slack"
],
"actionCount": 47,
"connectionCount": 4,
"firstSeen": "2026-01-15T09:00:00Z",
"lastSeen": "2026-03-30T14:00:00Z",
"createdAt": "2026-01-18T10:00:00Z",
"updatedAt": "2026-03-30T14:00:00Z",
"sopSteps": [
{
"id": "sop_001",
"stepNumber": 1,
"application": "Zendesk",
"instruction": "Open the ticket and read the customer's bug report.",
"detail": null,
"expectedResult": "Clear understanding of the symptoms and affected feature."
},
{
"id": "sop_002",
"stepNumber": 2,
"application": "Statuspage",
"instruction": "Check whether the affected component has a known incident.",
"detail": null,
"expectedResult": "Confirmed whether this is a known issue with a workaround."
},
{
"id": "sop_003",
"stepNumber": 3,
"application": "Sentry",
"instruction": "Search the customer's account for matching errors in the last 24 hours.",
"detail": "Match by user ID and error signature, not just error type.",
"expectedResult": "Confirmed whether the issue reproduces in their account."
}
],
"library": {
"id": "lib_xyz789",
"name": "Customer Support"
},
"edges": {
"outgoing": [
{
"id": "edge_001",
"targetPatternId": "pat_def456",
"targetPatternTitle": "Respond with workaround in ticket",
"label": "often next",
"actionCount": 29
}
],
"incoming": []
}
}Reasoning attached to every action
Pairs each captured action with the reasoning behind it, so agents see the why alongside the what.
What and why
The observed behaviour and the reasoning behind it.
Cause and effect
What initiates this pattern and what it produces.
Paths mapped between workflows
Builds a queryable graph of every workflow, showing what follows what and where decisions diverge.
Processes captured end-to-end
Records every step of every process your team runs, from the trigger to the final action, so agents have the full sequence to draw from.
Standard operating procedure
Step-by-step instructions to reproduce this pattern.
Zendesk
Open the ticket and read the customer's bug report.
Expected: Clear understanding of the symptoms and affected feature.
Statuspage
Check whether the affected component has a known incident.
Expected: Confirmed whether this is a known issue with a workaround.
Sentry
Search the customer's account for matching errors in the last 24 hours.
Match by user ID and error signature, not just error type.
Expected: Confirmed whether the issue reproduces in their account.
Linear
Respond with a workaround, walk through a config fix, or file a bug and tag the on-call engineer.
Enterprise customers get a Linear ticket regardless of resolution path.
Expected: Customer has the right next step and engineering is looped in where needed.
Slack
Post the resolution path in #cs-engineering for team visibility.
Expected: Team can flag recurring patterns across cases.
What your agents do differently
How agent decisions change with FusedFrames.
Reason from intent
Every pattern pairs observed behaviour with the reasoning behind it. When an agent hits a situation the steps don't cover exactly, it has the underlying intent to generalise from, allowing it to adapt correctly instead of failing silently.
Navigate branching workflows
Edges between patterns carry types and labels. Agents compare related workflows and understand which alternative applies in which situation, selecting the right path based on the conditions your team actually encounters rather than following a flattened linear runbook.
Verify each step
Every step in the graph carries the application used, the task performed and the expected result. Agents validate each stage against a known correct outcome before moving on, catching errors at the point they happen rather than at the end.
Ground decisions in evidence
Patterns are distilled from real observed actions, not written from memory. Agents reference actual examples and precedent when making a judgement call, drawing on what happened last time and why.
Scope its own execution
Trigger and outcome pairs give every pattern a clear entry point and exit condition. Agents never over-complete by running into the next process or under-complete by dropping off early.
Capture on your terms
Controls over capture, processing and sync.
Pauses on demand
Pauses capture from the menu bar whenever you need a break, and resumes the moment you're ready.
Strips sensitive data
Detects and removes sensitive data from every event before it leaves your Mac, with a second pass on our servers.
Snoozes when busy
Snoozes the reasoning prompts when you need focus, while still capturing every action in the background.
Captures every app
Records actions across the entire operating system, not limited to a browser or a fixed list of supported apps.
Reviews before distilling
Routes every captured action through manual review, so nothing becomes a pattern without explicit approval.
Splits into libraries
Organises captured patterns into separate libraries by department, function or any structure that fits your team.
Frequently asked questions
Start capturing today
Turn team know-how into agent context.
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