Context Engineering
Context engineering is the discipline of giving an agent the smallest possible amount of context that still preserves the information it needs to act correctly. Snipara's public platform combines retrieval, memory, orchestration, and task recovery to make that practical in production.
What This Guide Covers
Map the main context-engineering techniques to the public Snipara docs so users can move from theory to implementation quickly.
Core Principles
| Principle | Why it matters | Snipara surface |
|---|---|---|
| Just-in-time retrieval | Only load the most relevant slices of documentation at the moment of need | rlm_context_query, rlm_ask, rlm_search |
| Progressive disclosure | Start with summaries, then drill into exact chunks only when needed | rlm_sections, rlm_read, recursive context workflows |
| Persistent memory | Retain decisions, preferences, and evidence across sessions | rlm_remember, rlm_recall, group memory, memory tiers |
| Shared coordination | Keep multiple agents aligned on state, ownership, and task progress | Swarms, shared state, broadcast events, task queues, hierarchical tasks |
| Compaction and reuse | Reduce repeated token spend by caching, summaries, and structured state | RLM Runtime, summaries, analytics, automation |
Implementation Map
Recursive Context
Load broad context first, then progressively expand into exact evidence only when the current answer needs it.
Agent Memory
Persist facts, decisions, and validated learnings instead of reloading everything in every prompt.
Multi-Agent Coordination
Coordinate specialist agents with shared state, task queues, and recovery paths for abandoned work.
RLM Runtime
Use runtime caching and orchestration to reduce duplicate retrieval and repeated compute.
Practical Workflow
- Start with a narrow retrieval call such as
rlm_context_query. - Expand only the most relevant sections with
rlm_read. - Store durable decisions in memory instead of repeating large prompt prefixes.
- Use shared state or task queues when the work spans multiple agents.
- Recover stale or abandoned work explicitly instead of letting queues drift.