Product releases, platform milestones, and important changes across the Cortagent stack.
A public-safe engineering status note for Cortagent Agentic RAG: routing, retrieval, evidence selection, memory, caching, safety, and evaluation remain the active surface.
We clarified the boundary between conversation memory, retrieved evidence, and answer synthesis in the Agentic RAG runtime.
We shaped Agentic RAG evaluation cases around follow-ups, weak evidence, source targeting, and complex multi-part questions.
We tightened how retrieval diagnostics, evidence boundaries, and answer synthesis traces connect in the Agentic RAG loop.
We tightened the Agentic RAG path around avoidable work: repeated decomposition, repeated embeddings, and unnecessary retrieval fan-out.
Ingestion guardrails were added to make document handling and chunk quality more explicit before retrieval uses the content.
QA audit and benchmark tooling was added to separate measurable behavior from impression-based assessment.
A feedback store was added so retrieval and answer behavior can be improved from recorded outcomes instead of anecdotes.
Datasource routing work was added so retrieval can target relevant sources instead of searching every corpus the same way.
Keyword retrieval work gave the RAG layer a lexical path for exact terms, identifiers, and phrases.
Retrieval stitching work was added to join evidence from multiple retrieval paths without losing source boundaries.
Memory consolidation work was added to reduce duplicate memory state and keep useful context retrievable over time.
Structured answer work was added so synthesis can carry explicit fields instead of only free-form text.
Reranker selection work was added to keep evidence ordering separate from initial retrieval.
Prompt loading work made behavior dependencies easier to inspect instead of hiding important instructions inside ad hoc strings.
Language routing work was added so multilingual queries can be handled without treating language as a knowledge silo.
Graph retrieval modules were added so relationship-oriented evidence can become a selectable retrieval path.
KFE was positioned as an additional structured knowledge-access path alongside chunk retrieval.
Metrics and health modules were added around RAG infrastructure so operational state can be inspected.
Reflex policy hooks were added for pre-retrieval, post-retrieval, and post-generation checks.
A circuit breaker path was added so repeated retrieval or generation failures can be surfaced instead of retried blindly.
Safety validators were added around the RAG path to make invalid inputs and unsafe retrieval states explicit.
Vector integrity checks were added to catch invalid retrieval state before it leaks into answer synthesis.
RAG event journals and lineage artifacts were added so retrieval behavior can be inspected after a run.
We tightened the boundary between retrieval output and evidence selection so answer synthesis does not consume context blindly.
A pronoun resolution path was added to reduce failures on follow-ups like 'what about that' and 'what was her role'.
Conversation context handling was added so follow-up questions can reuse prior state instead of being treated as isolated prompts.
Semantic cache work added context checks so similar wording does not automatically mean the same retrieval state.
Decomposition results gained their own cache path so repeated complex queries do not always restart from zero.
Fingerprint caching was added as an explicit performance layer for repeated retrieval and reasoning inputs.
Vector and keyword retrieval paths were shaped into a hybrid orchestration layer with explicit diagnostics.
We added query complexity logic so decomposition can be based on explicit signals instead of defaulting every request into a heavy path.
A retrieval router baseline was introduced to make vector, keyword, graph, and swarm-style paths selectable instead of implicit.
Early work moved query handling toward explicit structure parsing so retrieval could respond to intent and complexity.
We started the Agentic RAG track by defining retrieval as part of the reasoning loop, not a one-shot pre-processing step.