Why Teams Adopt AgenticVision

Simulation date: 2026-02-23

Why this matters

Most production UI incidents are visual first and textual second.

If your team cannot query what users saw, incident response slows down and confidence drops.

Core capabilities (simple language)

1. Capture and store visual states
   • Keep screenshot-level memory in .avis artifacts.
2. Query and compare visual history
   • Find similar captures and compute diffs quickly.
3. Extract visible text (OCR)
   • Pull text from UI captures for audit and analysis.
4. Track quality and linkage health
   • Use quality metrics and linkage to memory where needed.
5. Control long-horizon storage growth
   • Budget policy can prune low-value captures over time.

Compelling scenario

A checkout button appears to "randomly disappear" in one environment.

Without AgenticVision:

• each person shares ad hoc screenshots and opinions.

With AgenticVision:

• captures are queryable,
• diffs are reproducible,
• OCR and similarity provide structured evidence.

That turns incident response into a process instead of a debate.

With vs without (real simulation)

Without

file <capture-or-store>

You can inspect metadata only. No visual-query pipeline exists.

With

agentic-vision-mcp info
agentic-vision-mcp repl
# /tools
# /validate

Observed simulation output:

• MCP tool surface reported with tool_count: 11
• tools included vision_capture, vision_query, vision_ocr, vision_diff, vision_health, vision_link
• REPL exposed interactive runtime validation commands

Numbers that make it real

From current docs/benchmarks:

• capture pipeline (file -> embed -> store): about 47 ms typical
• similarity search top-5: about 1-2 ms
• visual diff: sub-millisecond class
• MCP tool round-trip: around 7 ms typical

Long-horizon retention and tradeoffs

Budget policy can target ~1-2 GB over long horizons using:

• CORTEX_STORAGE_BUDGET_MODE=auto-rollup
• CORTEX_STORAGE_BUDGET_BYTES
• CORTEX_STORAGE_BUDGET_HORIZON_YEARS
• CORTEX_STORAGE_BUDGET_TARGET_FRACTION

Tradeoffs to understand:

• higher capture frequency and larger images increase growth faster
• OCR quality depends on source image quality
• cross-host workflows require explicit artifact sync (.avis/.amem/.acb)

What this means for technical readers

• You get scriptable visual operations, not manual screenshot threads.
• You can standardize visual regression and incident analysis workflows.
• You can manage storage pressure without deleting all history blindly.

What this means for non-technical readers

• Faster root-cause understanding for UI issues.
• Easier communication with before/after visual evidence.
• Less ambiguity in postmortems.

Multi-LLM fit

Claude, Gemini, OpenAI/Codex, Cursor, VS Code, and Windsurf teams can consume the same MCP visual capability surface.

Start in 5 minutes

agentic-vision-mcp info
agentic-vision-mcp repl

Success signal:

• your team can list tools, validate runtime state, and run visual workflows from one interface.