Enterprise RAG: architecture, access control and evaluation
Design traceable RAG with ingestion, revisions, authorisation, retrieval, context and citations. A security example and reproducible offline demonstration.
Technical guides
RAG, agents and memory: examine the architecture.
Eight guides on RAG, GraphRAG, multi-agent systems, context, memory and security. Architectures, examples, original references and a reproducible demo.
Choose the layer to examine.
Retrieval, context, memory and tool execution require distinct contracts. Each guide connects a design choice to data, permissions and a possible check. Examples are educational; executed observations concern the published demonstration.
Hybrid RAG search: chunking, reranking and evidence
Choose RAG retrieval for security documents: exact identifiers, lexical and vector search, rank fusion, reranking and evidence-level evaluation.
GraphRAG for cybersecurity: relationships, sources and permissions
Assess GraphRAG for security knowledge: local and global questions, relationship provenance, indexing costs, source changes and customer isolation.
Context engineering: budgets, evidence and agent compaction
Organise AI-agent context with token budgets, task state, tool outputs, evidence references and compaction. A security example and restart checks.
AI agent memory: writing, updating, forgetting and verification
Design AI-agent memory with task state, facts, provenance, contradictions, expiry and deletion. Security checks and research references.
Multi-agent systems: contracts, orchestration and recovery
Design verifiable multi-agent systems with task contracts, typed results, evidence, permissions, idempotency, budgets and recovery. A security workflow.
RAG security: prompt injection, memory and tenant isolation
Assess RAG security across hostile documents, persistent memory, permissions, caches and tools. Defensive test cases with expected evidence.
Evaluating cybersecurity AI agents: tasks, evidence and denominators
Build cyber-agent evaluations with scope, ground truth, precision, recall, abstention, unwanted effects and reproducible comparisons.
Examine a reproducible check.
The Python program executes 10 deterministic cases over fictional documents: permissions, organisation, revision, expiry and abstention. The recorded output reports 10 cases matching expectations. No model, embedding or network service is involved.
These results check the demonstration’s rules; they do not measure an AI agent’s resistance to attacks, generated-answer quality or a commercial product. Identity and metadata come from the fixture.
- Python source
- Fictional corpus and expected cases
- Recorded results and corpus hash
- Execution, scope and licence
python rag_demo.pyExplore international coverage.
This selection of press and specialist publications identifies topics discussed in the US, Russian-language community and China. Technical claims in the guides reference original papers and documentation. Company blogs and research coverage retain their declared source status.
- VentureBeat · GAM · US press · English
- VentureBeat · ACE · US press · English
- Habr · Память ИИ-агентов · Russian-language publication
- Habr · От RAG-прототипа к агенту · Russian-language experience report · company blog
- 机器之心 · MemSkill · Chinese publication · Chinese
Connect methods with your evidence.
Prepare HTTP exchanges, IOCs and evidence in the browser