Max Agency

Geng Sng is co-founder and CTO of Cogent, which builds autonomous agents that remediate vulnerabilities for enterprise security teams. Today, Cogent's agents process billions of security events per day, maintaining a live context graph of every asset and vulnerability across customer environments. In this conversation, Geng walks through Cogent's hot vs cold context split, the sub-agents that handle side quests, and the two graphs they run in parallel.
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We also discuss:
Why defensive security is harder for AI than offensive
Under the hood of Cogent's three agents
Inside Cogent's “read only” by-default sandboxes
Why graph databases don't scale for security data
Cogent Research and the move into formal verification
Why interactive agents need a deeper planning phase to one-shot
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Referenced:
Abnormal AI
Amazon S3
Anthropic
Bash
ChatGPT
Claude Code
Claude Mythos
CodeMender
Codex
Cogent
Cursor
Google DeepMind
GPT-5.5-Cyber
Jupyter
Letta
Mozilla
OpenAI
Opus 4.6
Opus 4.7
Vercel
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Where to find Geng:
LinkedIn
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Where to find Harrison:
LinkedIn
Twitter/X
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Where to find LangChain:
Website
Docs
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Send feedback or questions to [email protected]
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Timestamps:
00:00 Why mean time to exploit collapsed from years to minutes
02:08 Inside Cogent's Agent Lake architecture
05:11 Why Cogent rejected graph databases
10:48 The trust ladder before agents touch production
15:13 The three types of agents inside Cogent
17:07 How Cogent sandboxes its agents
19:16 Short-circuiting interactive agents with a deeper planning phase
24:31 What to do when users believe agents too much
31:21 Why sub-agents let agents go on side quests
34:59 Two-tiered evals and the metric that catches bad prompts
40:00 Cogent’s unique approach to context
48:39 Cogent Research and the move into formal verification
51:33 The single trait Cogent hires for
54:00 Open-sourcing models within six months
57:07 Why defensive security won’t be commoditized anytime soon
1:00:51 The founding insight behind Cogent

Alexander Shevchenko is the head of applied research at Ramp, where he leads Ramp Labs – the team behind Ramp Sheets and a steady stream of public AI engineering experiments. Ramp Sheets started as an internal process mining tool that turned Loom videos of accountants into Markov diagrams, before evolving into the agentic spreadsheet editor that shipped in November. In this conversation, Alex walks through the architecture under the hood, why Ramp biases the agent toward Excel formulas over Python code gen, and two recent Labs experiments: Latent Briefing and a user-steerable revival of Golden Gate Claude.

We also discuss:
Under the hood of Ramp Sheets
Inspect, Ramp's internal coding agent, and the self-improving monitor loop it powers
Why finance professionals rejected code gen as too "black box"
Why Anthropic models tend to excel at agentic spreadsheet manipulation
The case for putting the agent outside the sandbox, not inside it
The Loom-to-Markov-diagram process mining pipeline
RLMs and how subagents can share memory in latent space
Latent Briefing and KV-cache communication between subagents
Reviving Golden Gate Claude with steering vectors on Gemma

Referenced:
Alex Levinson
Anthropic
Ben Geist
Claude
Efficient Memory Sharing for Multi-Agent Systems via KV Cache Compaction (Ben Geist)
Gemma
Golden Gate Claude
Graphviz
Inspect
Latent Briefing
Loom
Modal
OpenAI
Opus
Qwen
Ramp
Ramp Labs
Ramp Sheets
Recursive Language Models (Alex Zhang)
Retool
Self-maintaining Ramp Sheets
Steer AI

Where to find Alex:
LinkedIn
Twitter/X
Website

Where to find Harrison:
LinkedIn
Twitter/X

Where to find LangChain:
Website
Docs

Send feedback or questions to [email protected]

Timestamps:
00:00 Introduction
01:13 The origin of Ramp Sheets
02:27 The Loom-to-Markov-diagram process mining pipeline
04:28 Why code gen approaches felt too "black box" to finance
06:13 Meeting finance where they already are: inside the spreadsheet
09:08 How far process mining got them
10:31 Text descriptions and Graphviz DAGs as output
12:41 Under the hood of Ramp Sheets
14:52 Why the agent uses Python only as an escape hatch
15:47 Why Anthropic models excel at agentic spreadsheet manipulation
17:12 Frankensteining the OpenAI Agents SDK
17:43 The Ramp Sheets UX and fast vs. expert mode
19:58 Agent in a sandbox vs. agent with a sandbox
21:55 Vibe evals with expert humans
23:40 Inspect, the internal coding agent
24:13 The self-monitoring loop and auto-PRs
28:01 Other wacky experiments on Sheets
28:43 Memory experiments that didn't pan out
31:16 Latent Briefing and KV-cache subagent communication
35:13 Reviving Golden Gate Claude
37:47 Contrastive pairs and steering vectors
39:47 Picking the right layers in Gemma
41:37 What Ramp Labs looks for when hiring

Florian Juengermann is the co-founder and CTO of Listen, an AI startup that turns qualitative research across hundreds of interviews, surveys, and focus groups into structured, traceable insights. Listen's agents analyze responses at scale, and Florian has rearchitected the system multiple times to get there. In this conversation, he walks through the virtual table architecture at the core of their Research Agent, how small models run map-reduce classification across thousands of open-ended responses, and the self-reviewing feedback subagent that catches errors during long async runs.

We also discuss:
The three agents inside Listen's platform
How Listen rearchitected from a simple RAG bot to a multi-agent system multiple times
Why the PowerPoint subagent was completely rebuilt using Claude's code SDK
Contextual prompt engineering as an alternative to skills
How Listen keeps report numbers live as new interview responses come in
When to trigger the long-running agent vs. showing early results
What Florian looks for when hiring agent engineers

References:
Anthropic
ChatGPT
Claude
Claude Code SDK
E2B
Emotional Intelligence
GPT Mini
Haiku
Listen
OpenAI
Pandas
Postgres
Python
Research Agent
Render
Zoom

Where to find Florian:
LinkedIn
Twitter/X

Where to find Harrison:
LinkedIn
Twitter/X

Where to find LangChain:
Website
Docs

Send feedback or questions to [email protected]

Timestamps
00:00 Introduction
01:25 The three agents inside Listen's platform
03:15 Live chat vs. long async runs, and how Listen tunes for each
05:33 Under the hood of the Research Agent
06:37 Listen's virtual table architecture
07:34 How small models classify thousands of open-ended responses
10:05 Running code in a sandbox: how E2B fits in
11:52 Why Listen rebuilt the PowerPoint subagent from scratch
14:11 Contextual prompt engineering instead of skills
16:32 The feedback subagent that reviews its own reports
18:14 How Listen runs evals in production
19:47 Unexpected ways users push the agent to its limits
21:42 How many times Listen has rearchitected, and why
24:59 Trace observability: depth over breadth
26:10 Lessons from running Claude Code SDK inside E2B
27:42 Memory: what's solved and what isn't
29:10 The Composer agent UX: co-editing a document with AI
35:50 How Listen keeps report numbers live as new responses come in
43:47 What Listen looks for when hiring agent engineers

Izzy Miller is an AI engineer at Hex, an AI analytics platform that was one of the first companies to ship data agents to real paying users. Today, Hex runs a multi-agent system with nearly 100K tokens of tools, and Izzy is building a 90-day simulation to evaluate whether those agents actually get smarter over time. In this conversation, he walks through the harness decisions that shaped their architecture, the failure modes Hex is seeing at scale, and what it takes to build an eval that no current model can pass.

We also discuss:
Why data agents are harder to verify than coding agents
Under the hood of Hex’s agents
How Hex is unifying separate agents
Why most eval sets are bad
The 90-day simulation for long-horizon evals
How Izzy went from marketing to AI engineer

References:
Andon Labs
Anthropic
Barry McCardel
ChatGPT
Claude Code
Claude Sonnet 4.6
DBT
GPT-3.5 Turbo
GPT-5.3 Codex Spark
GPT-5.4
Hex
LangChain
LangSmith
Looker
OpenAI
Opus 4.6
Satya Nadella
Snowflake
Vending Machine

Where to find Izzy:
LinkedIn
Twitter/X

Where to find Harrison:
LinkedIn
Twitter/X

Where to find LangChain:
Website
Docs

Send feedback or questions to [email protected]

Timestamps:
01:35 Where Hex's notebook agent started
03:46 The moment Hex knew it was time for agents
07:36 Why data agents are harder to verify than coding agents
09:30 How Hex is unifying separate agents
13:28 Under the hood of the notebook agent
15:41 The harness features that are now holding the agent back
17:41 Why Hex built their own orchestrator
18:59 Managing nearly 100K tokens of tools
20:49 Ephemeral queries and agent behavior trade-offs
24:46 The UX problem with showing agents' thinking
27:28 Why verification is harder than transparency for data agents
31:00 Memory, context conflicts, and collapse modes
34:38 How Hex built their internal eval system
39:29 Why most eval sets are bad
44:30 The 900% quota eval that every model fails
46:55 Model upgrades and the "in distribution" debate
51:34 How Izzy went from marketer to AI engineer
59:59 The 90-day simulation for long-horizon evals

Welcome to Max Agency, the podcast that goes deep into how the best agents are being built by builders like you. I'm Harrison Chase, CEO of LangChain, the agent engineering company, and I'll be your host.