LarkScout
LarkScout is an enterprise-grade knowledge platform for AI Agents: building upon multi-source ingestion, document parsing, and preview to perform Claim extraction, knowledge view compilation, contradiction scanning, and experience feedback. It compiles enterprise knowledge into traceable, consumable assets—preventing context window pollution and hallucination caused by dumping raw documents into RAG.
LarkScout
Knowledge & Skill CompilationEnterprise knowledge platform for AI AgentsDelivering compiled, traceable knowledge to Agents—not raw search hits.
- Compilation
- Compiled knowledge, not raw search hits
- Traceable
- Claim-level citations
- Closed Loop
- Contradiction scan + human-verified feedback
- Skill
- Aligned with agentskills.io
Typical pains
- Low-quality RAG recall floods Agent context windows with irrelevant noise
- Tacit project lessons fail to persist, causing Agents to repeat identical mistakes
- Agents continue executing on stale SOPs after corporate knowledge updates
Core value
One catalog for skills, SOPs and rules
The skills, SOPs and rules LarkScout compiles are listed in AULO's content library, each with its version, publisher, visibility and the agent classes it applies to.
Layered ownership for SOPs, rules, SKILLs, and personas
The content library organizes SOPs, rules, SKILLs, and personas across ownership tiers (My content, Department, Global), making deployed agents and versions immediately visible.
Promotion review and evidence trails
Promoting content from personal to department or global scopes requires review, with every request's rationale and operational evidence verifiable item by item.
Multi-source ingestion and execution tracking
Supports site imports, batch jobs, and scheduled recaptures, with ingestion progress, document counts, and detailed failure causes clearly displayed for each source.
Capabilities
Knowledge extraction
Ingestion
Agent content compilation
Safety & lineage
Does / does not
- Structured Claim extraction and knowledge view compilation: distill documents into citable fact units
- Contradiction scanning and experience feedback: detect conflicting knowledge, with Agent learning and human confirmation
- Multi-format parsing and enterprise connectors: mainstream documents plus Yuque / WeCom / Feishu sources
- Agent content compilation: Skill / SOP / Rule, native agentskills.io compatibility
- Batch SKILL Bundle import with a three-layer security gate: format checks, archive safeguards, and process-isolated YARA
- Protected sections and Official MCP knowledge search: fork lineage plus mTLS-authenticated retrieval
- Does not replace corporate Wiki or document management systems (it is the Agent-facing knowledge compilation layer)
- Does not directly mount files into Agent workspaces (HarnessServer distributes compiled content by role)
- Does not inject raw uncurated text into RAG pipelines (outputs structured Claims and traceable knowledge views)
By role
Structures enterprise knowledge into citable Claims and views rather than injecting uncurated documents directly into RAG
Fully traceable knowledge sources; MCP can be enabled over mTLS to governed NodalOS Agents, not as an unauthenticated public endpoint
Reusable lessons are verified by humans before deployment, eliminating cold-start trial-and-error for new Agents