Chain of Thought | AI Agents, Infrastructure & Engineering

Sudhir Hasbe is President and Chief Product Officer at Neo4j, the graph database company powering 84 of the Fortune 100 (Walmart, Uber, Airbus) at $200M+ ARR and a $2B+ valuation. Before Neo4j, he ran product for all of Google Cloud's data analytics services: BigQuery, Looker, Dataflow, and led the Looker acquisition.
His thesis: the hallucinations we blame on AI models are really a data architecture problem. LLMs weren't trained on your enterprise knowledge, so handing them a data lake with 10,000 disconnected tables and asking them to reason is the wrong design. The fix is knowledge graphs: feeding the model a structured map of relationships, entities, and context so it can reason over meaning, not just vector similarity.
Sudhir breaks down the five capabilities knowledge graphs unlock for enterprise AI: GraphRAG (moving accuracy from 60% to 97%), semantic mapping across siloed systems, context graphs, agent memory, and multi-hop reasoning. He explains three architecture patterns customers are actually shipping, why giving an LLM hundreds of tools makes it worse, and what Uber, EA Sports, Klarna, and Novo Nordisk are doing differently.
This is the case for treating knowledge as infrastructure.
We cover:
Why enterprise AI needs a different playbook than consumer AI
The five data asset types every agentic system needs: system of record, historical, memory, context, and reference
How GraphRAG combines vector search and graph traversal to move from 60% accuracy to 95%+
Three architecture patterns: semantic layer only, semantic map plus domain data, full consolidation (the Klarna/Kiki model)
What context graphs capture that Salesforce doesn't: the Slack and email negotiation behind every deal
Why giving an LLM hundreds of tools drops accuracy, and how Uber uses knowledge graphs as a business validation layer
What Neo4j's Aura Agent, MCP server, and A2A support mean for developers starting today
Chapters:
(0:00) Why building a self-driving car is hard
(0:22) Intro
(2:03) Hallucinations as a data architecture problem
(4:31) From models-as-core to systems-of-knowledge
(6:13) Why data lakes fail AI agents
(9:15) The five data asset types enterprise agents need
(11:46) Where basic RAG breaks down: the Spotify metadata lesson
(16:00) GraphRAG: 3x accuracy, easier development, explainability
(18:47) Semantic mapping across the enterprise estate
(19:23) Three knowledge-graph architecture patterns
(22:42) Context graphs: capturing the "why" behind decisions
(25:33) Individual vs. organizational agent memory
(28:40) Multi-hop reasoning for fraud rings and AML
(31:52) Why there are no shortcuts in enterprise AI
(36:38) What happens when you give an LLM 100 tools
(39:19) The Uber example: knowledge graph as business validation
(44:42) First mile of a 26-mile marathon
(48:32) Aura Agent, MCP server, and the A2A protocol
(50:43) Where developers should start
Connect with Sudhir Hasbe:
LinkedIn: https://www.linkedin.com/in/shasbe/
Neo4j: https://neo4j.com/
Neo4j Aura: https://neo4j.com/product/auradb/
Connect with Conor:
Newsletter: https://newsletter.chainofthought.show/
Twitter/X: https://x.com/ConorBronsdon
LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon
More episodes: https://chainofthought.show
Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at: 
galileo.ai/mastering-multi-agent-systems

Every few weeks at Microsoft, someone would build an AI prototype that blew everyone's minds. Three months later? Dead. "We can never ship that." Dan Klein watched this happen for five years before he decided to do something about it.
Dan is co-founder and CTO of Scaled Cognition, a professor of computer science at UC Berkeley, and winner of the ACM Grace Murray Hopper Award. His previous startups include adap.tv (acquired by AOL for $405M) and Semantic Machines (acquired by Microsoft in 2018), where he spent five years integrating conversational AI. His PhD students now run AI teams at Google, Stanford, MIT, and OpenAI.
At Scaled Cognition, Dan's team built APT1 (the Agentic Pre-trained Transformer) for under $11 million. It's a model designed for actions, not tokens, with structural guarantees that go beyond prompt-and-pray.
Dan makes the case that current LLMs are plausibility engines, not truth engines, and that the gap between demo and production is where most AI projects die.
Why prompting is a fundamentally unreliable control surface for production AI
How APT1's architecture gives actions and information first-class status instead of treating everything as tokens
The specific failure modes that kill enterprise AI prototypes within three months
Why stacking multiple models to check each other produces correlated errors, not reliability
How Scaled Cognition applied RL to conversational AI when there's no zero-sum winner
Why every S-curve in AI gets mistaken for an exponential — and what comes after the current plateau
The societal risk of systems that produce output indistinguishable from truth
Chapters
(0:00) Cold open: RL is about doubling down on what works
(0:28) Introducing Dan Klein and Scaled Cognition
(2:53) The demo-to-production gap: why AI prototypes die
(5:40) Why prompting is not a real control surface
(8:06) Modular decomposition vs. end-to-end optimization
(10:55) Are LLMs fundamentally mismatched with how we use them?
(14:26) What's wrong with benchmarks today
(20:27) APT1: building a model for actions, not tokens
(24:14) What makes data truly agentic
(28:02) Hallucinations as an iceberg — visible vs. undetectable
(34:16) Building a prototype model for under $11 million
(39:57) Applying RL to conversations without a zero-sum winner
(43:31) LLMs as a condensation of the web — and what happens when it runs out
(50:07) Reasoning models: where they work and where they don't
(53:04) Early deployments in regulated industries
(57:14) Why multi-model checking fails
(1:00:34) The minimum bar for trustworthy agentic systems
(1:04:07) Societal risk: when AI output is indistinguishable from truth
(1:13:33) Where Dan is inspired in AI research today
Connect with Dan Klein:
Scaled Cognition: https://scaledcognition.com
LinkedIn: https://www.linkedin.com/in/dan-klein/
UC Berkeley NLP Group: https://nlp.cs.berkeley.edu
Connect with Conor:
Newsletter: https://newsletter.chainofthought.show/
Twitter/X: https://x.com/ConorBronsdon
LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon
More episodes: https://chainofthought.show
Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems

Richmond Alake is Director of AI Developer Experience at Oracle and one of the most concrete voices on agent memory right now. His AI Engineer World's Fair talk on architecting agent memory crossed 100,000 views, he built the open-source MemoRIS library, and he co-created a course with Andrew Ng.
In this conversation, Richmond walks through memory engineering as a distinct discipline from prompt engineering and context engineering, demos a memory-aware financial services agent that runs vector, graph, spatial, and relational search in a single query, and explains the principle that separates production-grade memory systems from prototypes: don't delete, forget. If you're building agents that need to remember anything across sessions, this is the episode.
We cover:
- Why memory engineering deserves its own name, separate from prompt and context engineering
- The two failure modes Richmond sees most: wrong mental model and deleting instead of forgetting
- Four human memory types mapped to agent architecture: working, episodic, semantic, and procedural
- Demo: AFSA, a memory-aware financial services agent with converged search across data types
- How the Generative Agents paper's decay formula (relevance + recency + importance) enables controlled forgetting
- Where context engineering ends and memory engineering begins 
- Why files work for prototypes but databases win in production
Chapters:
(0:00) Memory is the last battleground in AI
(0:28) Meet Richmond Alake, Oracle's AI DevEx lead
(2:23) Why memory engineering is its own discipline
(7:57) The failure modes nobody talks about
(12:49) Demo: a memory-aware financial services agent
(18:30) Segmenting context windows by memory type
(19:22) Four human memory types mapped to agent architecture
(23:51) Procedural memory in production systems
(27:11) Don't delete, forget: implementing controlled decay (33:32) Sponsor: Galileo
(35:46) Where context engineering ends and memory engineering begins
(38:50) Is agent memory fundamentally a database problem?
(44:13) Files vs. databases: what production actually needs
(51:09) Picking your lane in the AI noise
(55:44) Richmond's courses with Andrew Ng, O'Reilly classes, and where to follow
Connect with Richmond Alake: LinkedIn: https://www.linkedin.com/in/richmondalake/
Check out his Youtube: https://www.youtube.com/@richmond_a
O'Reilly courses: https://www.oreilly.com/live-events/ai-memory-management-in-agentic-systems/0642572179274/
Diagrams from the episode: https://imgur.com/a/mMtcAtk
Connect with Conor:
Newsletter: https://newsletter.chainofthought.show/
Twitter/X: https://x.com/ConorBronsdon
LinkedIn: https://www.linkedin.com/in/conorbronsdon/
YouTube: https://www.youtube.com/@ConorBronsdon
More episodes: https://chainofthought.show
Thanks to Galileo — download their free 165-page guide to mastering multi-agent systems at http://www.galileo.ai/mastering-multi-agent-systems

Michel Tricot co-founded Airbyte, the open source data integration platform with 600+ free connectors that hit a $1.5 billion valuation. Now he's building the company's next product: an agent engine, currently in public beta. His thesis is that agents don't fail because models are bad. They fail because the data feeding them is wrong: context poisoning is killing them.
Michel demos this live. A simple Gong query through raw API calls burned 30,000 extra tokens and took three minutes. The same query through Airbyte's context store ran in one minute and used a fraction of the context window. Conor and Michel dig into why RAG alone won't cut it, what a "context engineer" actually does, how Airbyte tracks entities across Salesforce, Zendesk, and Gong without embeddings, and whether the SaaS apocalypse playing out in public markets is overblown.
Chapters:
0:00 Intro
0:20 Meet Michel Tricot, CEO of Airbyte
2:27 Data Got Us to the Information Age. Context Gets Us to Intelligence.
4:48 How Context Poisoning Breaks Agents
7:49 Why Airbyte Customers Stopped Loading Into Warehouses
10:12 Live Demo: Context Store vs Raw API Calls
10:38 What Does a Context Engineer Actually Do?
14:14 RAG Isn't Dead, But How We Build It Will Die
16:41 30K Wasted Tokens Without Proper Context
22:22 Cross-System Joins: Zendesk, Gong, and Salesforce
26:12 The Open Source Agent Connector SDK
29:45 The SaaS Apocalypse Is Overblown
36:09 From Data Pipes to Agent Infrastructure
38:51 What Agents Need to Get Right by Summer
40:48 Memory Is Just Another Form of Context
43:07 Outro
About the Guest:
Michel Tricot is the CEO and co-founder of Airbyte, the open source data integration platform used by thousands of companies to move data between systems. Before Airbyte, he led data ingestion and distribution engineering at LiveRamp. Airbyte raised at a $1.5 billion valuation and offers 600+ free connectors. The company recently launched the public beta of its agent engine, which includes a context store, agent connector SDK, and MCP integration.
Guest Links:
Airbyte
Michel on LinkedIn
Agent Blueprint (Substack)
Agent Connector SDK (GitHub)
Show Links:
Chain of Thought Podcast
Newsletter
Conor on LinkedIn
Conor on X/Twitter
Thanks to our presenting sponsor Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai.

Yujian Tang started the r/AI_Agents subreddit in April 2023. For the first year, it barely moved. Then it hit 9,000 members, he went on vacation, came back to 36,000, and now it's approaching 300,000. In this episode, Yujian talks about how that community grew alongside his event business (Seattle Startup Summit, 900+ attendees last year), his two failed startups, and why he just filed paperwork to launch his own venture fund.

Conor and Yujian dig into the mechanics of starting a fund from scratch (Delaware PO boxes, EIN numbers, lawyers), why AI startup valuations have doubled in the last two years, whether a one-person unicorn is realistic, and what failed founders learn that successful ones sometimes miss.

Chapters:
(0:00) Cold Open: The Subreddit Growth Explosion
(0:21) Intro and Meet Yujian Tang
(1:06) From AI Research to Community Building
(7:26) Where AI Applications Are Headed
(10:03) The AI Bubble and a Valuation Reset
(10:39) Getting Deal Flow Through Community Events
(14:02) Filing the Fund: The Boring Side of VC
(16:04) How r/AI_Agents Went from Crickets to 300K
(18:39) Building an Accidental Empire
(26:37) What Two Failed Startups Taught Him
(29:52) Why Pre-Seed Valuations Are Out of Control
(37:37) The One-Person Unicorn Debate
(39:50) Seattle Startup Summit 2026
(42:17) What Chain of Thought Should Cover Next
(43:25) Outro

About the Guest:
Yujian Tang is the founder of Seattle Startup Summit, the largest startup event in the Pacific Northwest. He created the r/AI_Agents subreddit (now nearly 300K members), runs hackathons and developer events across Seattle and the Bay Area, and is launching an early-stage AI venture fund.

Guest Links:
Seattle Startup Summit: seattlestartupsummit.com
Reddit: reddit.com/r/AI_Agents

Show Links:
Chain of Thought Podcast: https://chainofthought.show
Newsletter: https://newsletter.chainofthought.show/LinkedIn: https://www.linkedin.com/in/conorbronsdon/X/Twitter: https://x.com/ConorBronsdon

Sponsor: Thanks to Galileo. Download their free 165-page guide to mastering multi-agent systems at galileo.ai/mastering-multi-agent-systems