Context: Proactive Goal-Directed Intelligence via Composable Sandboxed Programs, Declarative Wiring, and Structured Interaction
arXiv:2605.23928v1 Announce Type: cross Abstract: We present Context, the intelligence layer of the Magarshak Architecture, which replaces reactive query-response chatbots with proactive goal-directed agents that advance shared tasks without waiting for user prompts. The architecture rests on three mutually reinforcing mechanisms. Write-time context assembly precomputes enriched typed attributes via Groker agents, assembling interaction context as a deterministic pure function of graph state; context blocks are byte-identical across turns between semantic changes, enabling near-100% KV-cache r
The proliferation of large language models is driving the need for more sophisticated agentic architectures that move beyond simple query-response interactions.
This development points towards a future where AI systems proactively manage complex tasks, significantly enhancing automation and human-computer collaboration.
The paradigm shifts from reactive chatbots to proactive, goal-directed AI agents capable of advancing shared tasks autonomously, without constant user prompting.
- · AI software developers
- · Enterprise productivity platforms
- · Automation solution providers
- · Simple chatbot providers
- · Manual workflow integrators
- · Legacy enterprise software
Increased efficiency in complex digital workflows through AI automation.
Disruption of traditional SaaS models as AI agents consolidate multiple software functions.
Re-definition of white-collar job roles towards supervision and strategic oversight of AI agent systems.
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Read at arXiv cs.CL