Every Cognilium AI engineering writeup grouped by subject area. Each hub starts with a foundational guide and continues into the supporting writeups.
When many agents beat one and when they do not: the decision gate, orchestration topologies, coordination and consistency, token and reliability cost, and evaluation. Building multi-agent systems that earn their overhead instead of paying for it.
Why production knowledge graphs decay and how to keep them correct: entity resolution, mislink detection, grounding checks, scoring, and scheduled audits. The engineering discipline behind knowledge graphs AI agents can trust.
How AI agents remember: working, episodic, semantic, and procedural memory, the limits of the context window, retrieval, and the knowledge graph as the long-term semantic store. The architecture behind agents that do not forget.
RAG → GraphRAG migrations, hybrid retrieval, security, and the patterns that ship when the vector DB stops being enough.
AgentCore, ADK, framework comparisons, multi-agent orchestration, and the deploy/observability stack production agents actually need.
Pipelines that turn unstructured PDFs (legal, financial, regulatory) into validated structured data — extraction, evidence-mapping, cross-document linking, and the multi-tenancy that makes it shippable.
Production voice systems on Twilio + ElevenLabs + Whisper — latency, compliance, and the architecture that survives a real call volume.
Eval suites, judge loops, smart routing, retry/circuit-breaker patterns, and the day-2 ops that keep a production LLM pipeline honest at scale.