System Architecture

AgenticVision

The Retina (Ingestion + Pre-processing)

AgenticVision operates with a persistent execution model instead of cold-starting browser infrastructure per request. Runtime contexts stay warm, so URL perception routes into already-active workers and returns faster than one-shot browser automation.

Operationally, the runtime can speculatively pre-fetch likely next surfaces based on current task trajectory. That means when an agent asks for the next page in a flow, the representation may already be prepared.

For recurring domains, runtime caches maintain semantic snapshots on a refresh cadence. First request can hit semantic state instead of full rendering, reducing latency and execution cost.

The Visual Cortex (Semantic Extraction Engine)

As pages resolve, extraction and structural encoding happen in parallel. The result is a machine-usable manifold:

• Content graph: entities, text blocks, tables, and semantic tags
• Action schema: interactable surfaces mapped into typed actions
• Navigation topology: link paths classified by purpose and boundary
• Visual skeleton: lightweight spatial structure for multimodal reasoning

This gives agents structure-first perception instead of screenshot-only interpretation.

The Agent Interface (Protocol Gateway)

The interaction model is intentionally minimal:

PERCEIVE  url=https://example.com
ACT       action_id=btn_submit payload={...}
TRAVERSE  link_id=next_page
OBSERVE   selector=.price

AgenticMemory

The Formation Layer

AgenticMemory persists cognition as structured graph events rather than flat text chunks. Facts, decisions, inferences, corrections, and skills are linked at write-time so retrieval stays contextual at read-time.

The Graph Brain

Each .amem artifact stores nodes + edges in a portable binary layout optimized for memory-mapped access. This supports low-latency traversal and keeps memory portable across providers and environments.

Query Model

The runtime is built around navigation and reasoning operations:

• traversal (why did I decide this?)
• temporal diff (what changed between sessions?)
• impact (what breaks if this belief is wrong?)
• context resolution (what is current truth after corrections?)

AgenticCodebase

Semantic Compiler

AgenticCodebase compiles repositories into semantic graphs where navigation follows concepts and relationships, not file-order text scans.

Impact + Coupling Intelligence

Before edits are applied, impact paths can reveal downstream consumers, likely breakage zones, and under-tested boundaries. Historical coupling signals surface hidden dependencies that static syntax alone does not reveal.

Temporal and Stability Signals

Codebase evolution is treated as time-series data. Stability, drift, and change-risk surfaces help operators sequence edits with lower regression probability.

acb compile ./repo
acb query impact --symbol User.email
acb query coupling --symbol PaymentService
acb-mcp serve

Cross-runtime contract

All three runtimes share these public constraints:

• standalone-first install and operation
• MCP-native integration for composition
• artifact portability (.amem, .avis, .acb)
• inspectable, reproducible operational behavior

What to add next

If you are expanding docs for technical trust and adoption, prioritize:

1. Architecture diagrams per runtime (request path + cache path + failure path)
2. Real benchmark methodology pages (hardware, dataset, reproducibility script)
3. Troubleshooting matrix by runtime/client/OS
4. Security and data boundary documentation per runtime