Loading...
Loading...
Design rationale
Why the architecture is shaped around ranked context, orchestration, local runtime control, and inspectable execution.
Practical systems grounding
This page explains the research and engineering principles behind that design. Context ranking, agent orchestration, event-driven coordination, local-first architecture, security boundaries, and token efficiency are core parts of how the system is built.
This is a practical reference, not a formal paper. A cited whitepaper is in progress, and if you want to suggest a source or discuss the ideas behind the architecture, reach out at support@chaos-dev.ai.
CHAOS context injection is not a simple vector lookup. The context layer draws on IR principles such as full-text search with term frequency weighting, relevance scoring, and recency decay to surface useful context for each agent call.
The PM orchestration engine decomposes goals into agent tasks, resolves inter-task dependencies, and schedules parallel execution where possible. The design borrows from build systems and compiler pipelines: topological ordering, critical-path scheduling, and queue-based work distribution.
Cross-clone synchronization in CHAOS is built on event-driven architecture principles used in distributed systems: a central event bus, append-only event logs, and fan-out broadcast to subscribing nodes. Each clone is an independent observer that reacts to published events — the same model behind message brokers and event sourcing systems.
The CHAOS agent workflow system encodes practical SDLC phases: planning, architecture, scaffolding, implementation, testing, review, documentation, and release. The goal is to make the expected path structured enough to inspect and repeat.
CHAOS treats retrieved context as untrusted input. The injection layer is structured to prevent retrieved content from overriding system instructions or hijacking agent behavior — a threat model grounded in research on adversarial prompting, indirect prompt injection, and LLM security. Context is formatted, scoped, and bounded before it reaches an agent.
Token budgeting in LLM applications is an active research area. CHAOS applies shared persistent context, cross-session memory, and ranked injection so agents do not repeatedly reload the same project knowledge from scratch.
The local-first design principle keeps operational state close to the user and makes sync an explicit choice. CHAOS applies this to AI tooling: context databases, session history, and agent state are local resources rather than mandatory cloud dependencies.
CHAOS draws on multi-agent systems (MAS) research for the design of the PM engine, agent specialization, and delegation patterns. Each agent is a bounded specialist — limited tools, defined scope, structured output — which mirrors the principle of minimal authority used in secure system design and in classical MAS architecture.