Topic
Entity Resolution
15 episodes
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Decision models like Jev handle what SQL rules cannot. Here's how data teams use them
Jessica and Cathy dig into decision models — specifically TypeSafe AI's Jev — and the argument that structured, typed ML judgments are the missing middle layer between SQL rules and full LLM calls in data pipelines.
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Agents need more than business process knowledge. They need to understand how your company actually works
SAP’s sponsored argument is that enterprise agents fail when they know the generic process but not a company’s messy, changing reality. The useful part is not the agent intelligence claim; it is the context, policy, reconciliation, and audit layer that makes execution safe enough to matter.
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Follow the Entities: A Corpus Map for Agentic Search
Vince and Ava break down CorpusMap, an offline entity layer that gives agentic search a reusable map of a document corpus instead of forcing every query to rediscover the same cross-document links. They focus on the product value, the mechanics of entity pages and document traversal, and whether this looks shippable beyond a paper demo.
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Adding Temporal Reasoning to Graph RAG: Tracking Fact Freshness and Staleness MachineLearningMastery
Vince and Ava debate a MachineLearningMastery article that adds timestamps and recency decay to Graph-RAG, treating it as a useful retrieval primitive but not a complete truth mechanism.
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SpeakerMem R1: Speaker Centered Dual Track Memory for Multi Party Dialogue
SpeakerMem-R1 tackles a real gap in long-term memory for group chats: keeping speaker attribution, person/group scope, and evolving state intact instead of flattening everything into a relevance search problem. The episode leans on the paper’s dual-track design, Anchor–Separate–Resolve–Compose query flow, and Writer-R1 training setup, while staying grounded on whether this is actually shippable for multi-party agents.
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GraphRAG: A Practitioner's Guide to 6 Advanced Architectural Patterns
Pippa and Tyler dig into a GraphRAG guide that’s really arguing for a more honest question: not “does GraphRAG work,” but “which retrieval shape matches the query shape?” They focus on the production trade-offs between strict graph querying, hybrid vector-plus-graph setups, and when knowledge graphs actually earn their keep.
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Orchestration is the new challenge for CX in the age of AI agents
Vince and Ava unpack a sponsored VentureBeat piece arguing that orchestration and shared context layers are now the main CX challenge in the age of AI agents. They separate legit architectural points—enterprise ontologies, context graphs, network latency—from marketing around Tata’s Interaction Fabric, and talk about who actually needs to care and what changes in practice for CX and IT teams.
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Making the Knowledge Layer a Graph You Actually Traverse
Vince and Ava examine a redesign of a persistent knowledge layer that retires keyword-based routing in favor of always-fused retrieval, graph traversal, bitemporal edges, discovered contradictions, and stronger entity resolution.
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Overview: Entity Resolution
We finally slow down and unpack entity resolution: how systems decide that messy, differently formatted records point to the same real-world thing. We use one front-desk mental model to get from duplicate customer records to confidence scores, blocking, and safer linking decisions.
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Vector RAG vs Graph RAG: Which Fits Best? | EM360Tech
Pippa and Tyler break down the real-world choice between Vector RAG and Graph RAG for enterprise AI: Vector RAG shines in straightforward retrieval but falters when the answer lives across relationships, while Graph RAG brings structure and richer answers—for a cost. The takeaway? Hybrid approaches are quietly becoming the practical default.
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What can you do with OpenTelemetry entity events?
Fern and Lintel dig into OpenTelemetry entity events as the missing inventory layer in observability: a stream of what exists, how it changed, and how to query it over time. They like the event-sourced, bi-temporal graph idea, the exact-identity discipline, and the move to expose it through GraphQL and MCP, while noting the model is still evolving and producers have to get identity right.
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Overview: Graph based Memory Representation
We finally slow down and explain graph-based memory representation, the thing we keep gesturing at whenever agent memory, receipts, and relationship-aware retrieval come up. We use one corkboard mental model to make nodes, edges, traversal, and the real trade-offs feel less mystical.
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Building Knowledge Graphs with Claude: A Prompt Based Engineering Playbook
Anthropic's knowledge-graph engineering playbook replaces classical NLP pipelines (trained NER, relation classifiers, entity-resolution heuristics) with a sequence of Claude API structured-output calls. The entire extraction-resolution-assembly-querying loop becomes prompt-based, scaling from Haiku (high-volume extraction) to Sonnet (reasoning). The graph serves multi-agent systems as shared memory, grounding layer for evaluator-optimizer loops, and persistent world model across sessions. The paper maps this onto Anthropic's five canonical agent patterns and reports precision/recall against a gold set.
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Why AI Company Brains Fail: Beyond Vector Search and GraphRAG
Pippa and Tyler unpack why a cheap vector search demo breaks on broad portfolio and exact counting questions, and why the article’s lighter entity layer may be more practical than a full GraphRAG stack.
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The No BS Guide to Build a Context Graph
Najmuzzaman's 'No BS Guide to Build a Context Graph' lays out how a production context infrastructure moves beyond CRM data to capture decision traces, organizational patterns, and lived heuristics. The real breakthrough is surfacing context and patterns that aren't captured in playbooks, making organizational intelligence accessible and explainable. The hard technical work: entity resolution, permissioning, and evolving patterns transparently.