Most MCP servers in production today are stateful- each client session is pinned to one server instance.
That works on a single node. Add a load balancer and scale horizontally, and sessions start breaking: Kubernetes routes a request to the wrong pod, a Fargate deployment rolls and drops active connections, and NGINX or AWS ALB need special configuration just to keep the server reachable.
The 2026 spec release candidate, locked May 21, fixes this at the transport layer through three SEPs. - SEP-1442 removes the mandatory init handshake so negotiation folds into the first request. - SEP-2322 makes elicitation stateless. - SEP-2243 mirrors routing data into HTTP headers so load balancers can route without parsing the payload.
This session covers what each SEP changes, what breaks in the server you run today, and what to refactor before the spec ships. I'll also show how we externalize session state at Mem0, so a stateless server keeps the memory and context a real agent needs.
You'll leave with a migration checklist and an architecture for stateless-first MCP.
Authorization in MCP has been through a number of revisions in the past year. Some major (remember when the authorization server and resource server were expected to be the same?) and some minor. As the specification has stabilized, it can be difficult to know: Am I doing it right?
In this talk we'll briefly recap where MCP Auth came from and where it is now, and then we'll dig into Auth best practices based on evolving discussions and connecting Claude.ai to loads of MCP servers in practice.
We'll cover things like: client registration, progressive authz with challenges, and enterprise managed auth to leverage existing SSO connections.
We'll also explore the latest extensions in development and how to put them together into a cohesive solution for human-in-the-loop but also agentic and on-behalf-of flows.
Lastly, we'll cover how to leverage MCP's conformance testing suite to see how closely you're following best practices, using either client, server or authorization server test suites.
Paul Carleton is a Core Maintainer of the Model Context Protocol and Auth Nerd at Anthropic, where he leads auth implementations across Anthropic's clients and the TypeScript and Python SDKs. He drives MCP conformance testing efforts to ensure consistent behavior across the ecosy... Read More →
Thursday September 10, 2026 10:40 - 11:05 JST Hall 1F
The MCP 2026-07-28 release candidate introduces a major shift in how agents connect, authenticate and manage state. With a stateless protocol core, first-class extensions and hardened authorization, the new spec improves scalability and interoperability, but it also changes the security model in ways that many builders may not fully anticipate. In this talk, I will examine the release candidate through a defender’s lens and highlight the practical risks that emerge when session state disappears, trust boundaries move and extension-based flexibility expands the attack surface. I will discuss failure modes such as broken state assumptions, capability confusion, unsafe cross-client behavior, and authorization mistakes in real deployments. The session will also cover concrete hardening patterns for secure MCP adoption, including least-privilege tool design, explicit state handling, authorization scoping, telemetry and rollout validation. Attendees will leave with a practical checklist for evaluating stateless MCP systems and defending agent runtimes in production.
Quality Analyst, MCP and AI Security Researcher, Thoughtworks
Thejes Sree Satheesh Kumar (she/her) is a Quality Analyst – Consultant at ThoughtWorks specializing in application and AI security testing. A CEH, CompTIA Security+ professional, With a strong background in automation testing (like Playwright), she combines quality engineering and... Read More →
Srinivasan Sekar is a Director of Engineering at TestMu AI (formerly LambdaTest). He loves contributing to Open Source. He is an Appium Member and Contributor to various open-source repositories like Selenium, Webdriver.io, taiko, etc. He worked extensively on testing various Microservices... Read More →
Thursday September 10, 2026 11:15 - 11:40 JST Hall 1F
The first wave of MCP adoption was defined by abundance. Every API endpoint became a tool, every workflow became a collection of tool calls, and many servers exposed hundreds of operations directly to agents. It worked, but at a cost: bloated context windows, slower tool selection, and increasingly unreliable agent behaviour.
But then Code Mode appeared!
With approaches Code Mode agents can search an API surface, generate code, and interact with services directly. If models can consume entire APIs and write code to acomplish a task, does tool design still matter?
In this talk, we'll compare three approaches to agent tooling: curated workflow-oriented tools, tool discovery and search, and Code Mode's search-and-execute model. Using real-world examples, benchmarks, and evals, we'll explore the trade-offs in reliability, latency, token usage, and task success.
Attendees will leave with a practical framework for deciding when to consolidate tools, when to expose APIs, and whether the future of MCP is better tools or fewer tools altogether.
Ruben Casas is a Google Developer Expert and Staff Engineer at Postman. He builds developer tooling, AI agents, and MCP apps, and writes about frontend architecture at scale and AI. Ruben is a podcast guest and has spoken at major conferences, sharing insights from large-scale migrations... Read More →
Thursday September 10, 2026 13:30 - 13:55 JST Hall 1F
AI agent discussions often focus on coding or MCP. My starting point is different. I use coding agents such as Claude Code and Codex as assistants for work beyond development. Instead of automating isolated tasks, I keep them beside me as a secretary and knowledge platform for routine operations, speaking requests, internal coordination, slide preparation, and customer-facing project delivery.
That daily use reveals agent-worthy problems. With Markdown, Git, Google Workspace, and MCP in the loop, work becomes context an agent can read, improve, and hand back. I then turn those problems into agents I build, deploy, review, and operate myself.
I will cover Vibe Presales, my term for turning a customer's concerns, constraints, and reactions in a presales conversation into a working demo while the context is fresh. I will also share lessons from a PowerPoint-building agent, including design, deployment, review, and output quality for real slide and proposal work.
These lessons become OSS code, articles, books, and hands-on material. Attendees leave with a loop for living with agents at work, building reliable systems, and sharing reusable lessons.
What happens when an AI agent has to pay for its tools? Today, nobody knows. Open-source MCP servers run for free, and their maintainers quietly absorb the cost. The x402 protocol changes the model: a server can charge a small payment for each tool call, with no accounts or API keys needed. The plumbing works. The open question is how agents behave once tools have a price.
We put it to the test. We ran a paid MCP server for several weeks and watched agents act as customers. We changed prices to find the point where agents stop calling a tool. We offered the same tool free and paid, side by side, to see whether agents compare prices or just call the first match. We let spending budgets run out mid-task to see what agents do when money gets tight. And we compared all of this across four agent clients: Google ADK, LangGraph, the OpenAI Agents SDK, and Claude Code.
The result is practical guidance for maintainers: what to charge, where the payment flow breaks, which framework behaviors to expect, and whether per-call payments can realistically fund an open-source MCP server.
Prakash Rao is a Principal Cloud Engineer at AIG Technologies in Japan, designing Google Cloud infrastructure for financial services: hybrid connectivity, enterprise networking, and security architecture. His recent work includes deploying AI agents on Vertex AI Agent Engine and infrastructure... Read More →
Senior Software Engineer at Red Hat with 13+ years of global experience in 3G, 4G, and 5G network integration. US patent creator and Telco Automation expert. 2026 AAIF ambassador and active CNCF Program Committee member and speaker at KubeCon, Kubedays, and CNCF events. Contributor... Read More →
Thursday September 10, 2026 14:40 - 15:05 JST Hall 1F
Every team building agentic systems hits the same wall: the gap between a demo and a system you'd trust in production. Most assume it's an engineering problem. It isn't. It's a governance problem and governance in a multi-agent system isn't a policy document. It's the full data path.
We demonstrate a production-style multi-agent workflow where customer feedback flows through agents, MCP tools, GitHub integrations, and multiple LLM providers before triggering automated actions.
We show how cloud-native primitives govern every interaction: OpenTelemetry for distributed tracing across agent hops, OPA for declarative policy enforcement, API gateways for prompt injection protection and model routing, and Kubernetes for workload isolation.
The hardest part of running agents in production isn't the AI. It's the same problems Kubernetes solved for microservices, observability, traffic management, policy, workload isolation applied to a new class of workload the ecosystem is still learning to instrument.
Juhi is the Developer Evangelist (APAC) at Kong. She’s passionate about helping the developer community and building strong connections. Outside of work, Juhi enjoys reading, staying fit, trekking, traveling, and cooking. She describes herself as a "product of a community" with... Read More →
Senior Community Manager & Developer Advocate, Mirantis
Prithvi Raj is a Senior Community Manager & Developer Advocate at Mirantis working with the OSPO. He is a CNCF Ambassador with 6+ years of experience who helped scale a CNCF incubating project and runs the CNCG Platform Engineering Meetup group. He is currently leading the community... Read More →
Thursday September 10, 2026 14:40 - 15:05 JST Hall C
A production issue spans multiple cloud environments. Everything looks fine in isolation, but the system behaves unexpectedly end-to-end. Reconstructing what is actually deployed requires stitching together cloud consoles, infrastructure definitions, and internal tooling.
AI agents could help with this kind of problem, but they need better context. Infrastructure is a key part of that context gap. Cloud resources, environments, configurations, and policies are essential for reasoning and decision-making, yet this information is fragmented across infrastructure-as-code, cloud providers, and internal systems.
This talk explores why a developer platform was built: to unify infrastructure management, standardize how teams provision and manage cloud resources, and provide a consistent abstraction across providers, accounts, and environments. The platform then evolves into a unified source of infrastructure context, aggregating metadata into a coherent infrastructure blueprint. By exposing this through an MCP server, it allows AI agents to understand the full infrastructure landscape, improving reasoning, troubleshooting, and the quality of assistance in engineering workflows.
With extensive experience in the software industry, Gang has worked across Platform Engineering, Infrastructure Engineering, and Site Reliability Engineering. His focus is on building scalable platforms, enabling developer productivity, and driving operational excellence within fast-growing... Read More →
Throughout her career, Kristina has written code, managed teams, and optimized delivery. As an SRE Product Manager at Electrolux Group, she helps the platform team adopt product management practices. Her mission is to elevate the developer experience by treating internal developers... Read More →
Thursday September 10, 2026 16:10 - 16:35 JST Hall C
MCP grants AI agents access to file systems, Git, cloud, and SaaS. Yet permission models — IAM, OAuth scopes, PATs — were built for humans and fall short for AI.
The issue isn’t granularity. Existing permissions assume “human rationality” as a boundary: write access doesn’t mean a human wipes a repo; CI write access doesn’t mean a human exfiltrates secrets. Permissions stay broad because human judgment fills the gap. AI lacks this buffer — through prompt injection or edge cases, it acts destructively where a human would stop. Per-call approval collapses under fatigue.
As an SRE on strong production credentials, my question wasn’t “how to restrict AI” but “how to maximize safe delegation.”
I propose Intent as Code: coding what was left to human rationality. Three OSS tools: safe-push rejects pushes touching .github/ or others’ commits, preventing CI hijacking safe-gh wraps gh CLI with conditions like “only own issues” or “only approved PRs to develop” — expressing intent PATs can’t safe-webfetch uses Claude Code Hooks to auto-allow learned-safe URLs, cutting fatigue decisions
Attendees leave understanding why permissions fall short for AI, with patterns to apply.
The MCP spec is clear about how one client and one server talk to each other. It says much less about what happens when the client is itself a distributed system, serving millions of people across thousands of third-party MCP remote servers.
At Anthropic, we run a proxy between our products and a large, growing set of remote servers. At that scale, we started running into problems the spec doesn't answer.
This talk is about three of them:
- Authentication: How do we make authentication work across servers that all implement it a little differently? How do we keep people connected when connections drop and credentials expire, without making them sign in again?
- Caching: How do we avoid hammering the MCP remote servers, and which caching strategies make that possible?
- Monitoring: How do we monitor a system where the servers vary widely, from stable production ones to unstable ones still in development? When something breaks, how do we tell whether the fault is ours or the server's?
Camila Rondinini is a Member of Technical Staff on the Connectivity team at Anthropic, where she works on supporting MCP across Anthropic's product surfaces. Her focus is on improving the reliability and monitoring of MCP at scale.
Thursday September 10, 2026 16:45 - 17:10 JST Hall 1F
AI agents have long been capable of interacting with the web, relying on brittle DOM scrapers and heavy browser automation has kept these workflows slow and prone to failure. We will explore how the WebMCP shifts agentic web interaction from merely functional to highly performant and efficient by providing a standardized, machine-readable layer for context sharing and action execution. WebMCP eliminates the friction and overhead of legacy automation techniques.
Vin is the co-founder of StaffOS, an AI Workforce platform for business to deploy and govern AI agents. He is also a Google Developer Expert, having partnered with Google to advocate web technology for the past 10 years, speaking, mentoring and conducting community workshops.
Thursday September 10, 2026 17:20 - 17:45 JST Hall C
Your agent swarm is running. It's browsing the web, calling APIs, reading files, making decisions, all on behalf of your users and your organisation. But ask yourself: do you know which agent did what? Can you revoke access to just one of them? Can you prove to an auditor that your agents only touched what they were supposed to?
Probably not. Because most agentic deployments treat agents as anonymous processes : no identity, no credentials, no audit trail, no way to fire them when something goes wrong. If a human employee operated this way, you'd call security. For agents, we call it production.
This talk proposes a fundamentally different approach: onboard your agents like employees. Every agent in your swarm deserves a verifiable identity, scoped permissions, a centralised audit trail, and an offboarding process. Drawing from experience securing cloud native and AI infrastructure, this session presents a blueprint for an open source "Okta for agents"! composing Identity Providers, API gateways, and policy engines to bring the same governance we give humans to the systems acting on their behalf.
You hired 1000 people. It's time to learn their names.
Mart is an Infrastructure Security Manager at Bitso, where he enjoys managing various engineers who teach him every day how to break things and become a better manager and engineer. Mart began his journey in cybersecurity trying to understand why so many people liked prime numbers... Read More →
Friday September 11, 2026 10:40 - 11:05 JST Hall 1F
AI-powered incident response sounds like the perfect use case for autonomous agents, but giving them unrestricted access to raw alerts and logs can quickly turn automation into hallucination. Whether you're debugging production outages or investigating failed deployments, engineers still spend hours manually correlating signals across disconnected observability tools.
In this talk, we'll break down the anatomy of a reliable agentic incident investigation system and show why structured incident context outperforms raw telemetry, how evidence enrichment improves reasoning, and why bounded context is critical for trustworthy AI agents.
We'll walk through production-ready patterns for building dependable operational agents: 1. Structured evidence gathering from Datadog, New Relic, Splunk, and Prometheus 2. Context engineering through alert correlation and deployment metadata 3. Long-term memory using historical incidents stored in a vector database 4. Human-agent collaboration that validates AI-generated RCAs and continuously improves future investigations.
Thus helping you reduce manual triage, lower MTTR, and build reliable, production-ready AI-driven incident response systems.
I'm Madhu Patel, a Software Development Engineer at Adobe, where I build large-scale distributed backend services for Creative Cloud and AI-powered platforms.
I graduated from Indira Gandhi Delhi Technical University for Women, one of Asia's largest technical universities for women. My interests include cloud-native architectures, AI agents, Kubernetes, and microservices, with a focus on building secure, scalable, and resilient systems... Read More →
SDE 3 with 5 years' experience at the intersection of GenAI, backend engineering, and cloud platforms. I own core GenAI services, building production-grade ML APIs/SDKs, and shipping 0→1 products from alpha to GA. Currently focused on cloud-native AI infrastructure: architecting deterministic agent workflows on Kubernetes, driving GitOps with ArgoCD, and scaling resilient platforms f... Read More →
Friday September 11, 2026 11:15 - 11:40 JST Hall C
Graph-based code intelligence (not GraphRAG) is the infrastructure primitive that AI coding agents need to reason about large codebases without burning tokens. In this session, we'll evaluate various approaches and identify where the field is converging.
Agents fall back to grep because retrieval solutions return a probabilistic approximation of the code at a given point in time. The context goes stale on changes and loses fidelity when going from code to embeddings. By exposing a call graph, based on established compiler theory, to the LLM through a series of functions and tools, the LLM doesn't need to waste tokens because it has access to deterministic, structured, quick access data from which it can get cheap, reliable answers.
Sourcegraph, Meta's Glean, Google's Kythe, GitHub's Stack Graphs, and newer efforts like GitNexus and Aleutian Trace are all building graph based offerings for LLMs. This session provides a deep dive into the problems faced by agents working in large codebases, maps the solution space of graph based coding tools vs more common LLM solutions— RAG, GraphRAG, pure traversal, embeddings — and then shows where each wins or breaks.
Founder of Aleutian AI, former Apple Data Engineering Manager, Former McKinsey Data Engineer. Focused on data and AI privacy research for large scale data processing and analysis.
Friday September 11, 2026 11:15 - 11:40 JST Hall 1F
Every agent system is two things: the model, which you rent, and the mask - the character - which is yours. The industry keeps upgrading the model and expecting the mask to improve. It doesn't work that way.
I'm a LAMDA-trained actor turned agent engineer. This talk is the actor's toolkit applied to shipped systems. Case one: a comms pipeline in production at a major crypto protocol - the generating model does Stanislavski table-work (super-objective, through-action, lining) before writing a word; a separate director model audits it blind; and voice is scored by a Laban Movement Analysis classifier that grades the human-written corpus and the machine's output on the same instrument. Brand voice becomes a falsifiable measurement. Case two: the home lab - eleven agents built with a Stanislavski character method, orchestrated over MCP, running my actual work.
You'll leave with the model/mask distinction, a character pipeline you can copy, and the failure modes: drift, voice collapse, paint-by-numbers slop.
Nick Howden-Steenstra trained at LAMDA and spent years building characters on stage; now he builds them as infrastructure. At a major crypto protocol he made brand voice falsifiable - generation gated by Stanislavski table-work, output graded by a Laban Movement Analysis classifier... Read More →
Friday September 11, 2026 11:50 - 12:15 JST Hall 1F
The AI industry has moved so fast that we are still evaluating probabilistic software using the same deterministic metrics we applied to traditional code. As a Product Analyst working on AI agents at PagerDuty, I saw a critical need for a new observability standard, one that moves beyond clicks to measure true reasoning and reliability. To address this, I’ve developed and open-sourced a specialized framework designed to help teams decide, with data, when to hire, train, or fire an AI agent. In this session, I will walk through the H.I.R.E. Framework methodology and share the technical architecture of an evaluation pipeline that turns qualitative conversational data into structured, actionable product insights. I will share the open-source repository containing these metric definitions and templates, providing resources for the community to move past agent-washing and toward building verifiable, trustworthy agentic systems.
Inês Bolaños focuses on the intersection of AI, product strategy and data reliability. With almost a decade of experience, she specializes in turning complex data into actionable product decisions. Combining a background in Communication with a Master’s in Big Data, Inês os helping... Read More →
Friday September 11, 2026 13:30 - 13:55 JST Hall 1F
"Every MCP server, skill, and plugin in your coding agent is code or instructions you never really reviewed, running with your access to your database, your repos, and your keys. Three different ways in, and almost nobody is watching any of them."
Most of the agent security conversation is about prompt injection. Meanwhile there are three underserved vectors sitting right inside the tools you use every day:
- **MCP servers** run with the same access you have. One can look clean when you approve it and change its behavior later, or pull in a compromised dependency on the next run. - **Skills and instruction files** (`SKILL.md`, `AGENTS.md`, `CLAUDE.md`, rules files) are just text. Nothing to install, nothing to flag. They quietly reshape what your agent does, and hidden instructions, zero-width characters, and pipe-to-shell tricks ride in the same way. - **Plugins** bundle the first two together and ship through marketplaces with uneven vetting, so a single install can bring in both an MCP server and a skill you never opened.
I'll show where each one lives on disk, why the usual tooling misses it.
Alexander Frazer is a Founding Security Engineer at Runlayer, where he works on securing AI agents and the MCP ecosystem. He chairs the AAIF Security & Privacy Working Group, leading the effort to build community consensus around agentic security and privacy standards and best practices... Read More →
Friday September 11, 2026 14:05 - 14:30 JST Hall C
In the agentic AI space, standards like MCP and A2A and a growing OSS ecosystem are evolving. Yet building a real multi-agent system requires more than a single framework or protocol. It involves integrating multiple OSS components and adjacent specifications across areas such as agent-to-agent communication, orchestration, authentication, and observability. There is limited shared understanding of how to combine them and what architectural considerations and integration gaps emerge.
This session shares our work on organizing the landscape of OSS-based agentic AI stacks, and on designing architectures built around MCP, A2A, and representative OSS, including prototyping through reference implementations. We explore several architectural patterns from a whole-system perspective. With enterprise use in mind, our scope also covers cross-cutting concerns such as authentication and observability. (LLMs are currently proprietary.)
Through this work, we examine what each OSS layer solves and does not solve, and highlight cross-layer concerns that emerge only when components are integrated as a multi-agent system. We share these observations with a demo of the reference implementation.
Tatsuya Sato is a Chief Researcher at Hitachi, Ltd. His research focuses on agentic AI platforms, blockchain platforms, system operations, and OSS. He has been an active OSS contributor for many years. He has contributed extensively to the LFDT and Hyperledger communities and continues... Read More →
The speaker is a researcher and engineer at Hitachi, engaged in open-source software development. Initially focused on blockchain technologies, they have worked on Self-Sovereign Identity (SSI), Decentralized Identifiers (DID), and NFTs. In recent years, their research has shifted... Read More →
Friday September 11, 2026 14:05 - 14:30 JST Hall 1F
Most "human-in-the-loop" agent systems assume the human can specify success up front: a passing test, green CI, a structured eval. What if the human's taste is the spec?
This talk presents YouAndOrchestra (YaO), an open-source agentic music composition system on Claude Code. YaO orchestrates seven role-based subagents — Producer, Composer, Harmony Theorist, Rhythm Architect, Orchestrator, Mix Engineer, Adversarial Critic — turning natural language into a score evaluated across six dimensions, every note carrying provenance.
https://github.com/shibuiwilliam/YouAndOrchestra
Three collaboration patterns from iteration: 1. Three-tier feedback. Users rewrite the YAML spec, regenerate a section, or pin feedback to a bar, beat, and instrument. Choosing the level is itself a UX problem. 2. Conductor loop with a critic gate. Generate, evaluate, adapt, regenerate runs up to three iterations, with critics gating before notes are placed. 3. Provenance as trust substrate. /explain queries an append-only causal graph — "why did the chorus modulate to the relative minor?"
Attendees leave with patterns and open-source code for agent systems where the human stays in the seat of judgment.
Yusuke Shibui works across MLOps, data, infrastructure, backend, research engineering, and tech sales — and lives with two cats. His current focus is putting generative AI and AI agents into production. He is the author of "Machine Learning System Design Patterns" and "A Practical... Read More →
Friday September 11, 2026 14:40 - 15:05 JST Hall 1F
AI coding agents now generate UI from component libraries. Most teams write component markdown files and expect agents to take it from there. The docs were complete. Agents still got it wrong.
Sitting with engineers in code review, the same things kept surfacing: the agent picked a visually similar but wrong component; it invented props; it read "Tag" as a UI primitive when the codebase meant a security concept. One author. No review process. A library too large for any agent to navigate without a map.
Not a component library talk. A talk about what the workflow was missing when the docs were already done.
I'll walk through the workflow that came out of those sessions: why a component file needs more than one author before agents can trust it; how an index changes what agents can find; and the naming decision that was invisible to humans and catastrophic for agents. Including the moment the team realized the problem wasn't the docs.
You'll leave with one reframe: a component library has always had two users. It now has a third. Here is what the workflow looks like when you build for all three.
Karen Ng designs AI-driven products for security engineers and oncologists. She leads UX at Endor Labs, where she has built AI agent governance, code scanning, and security workflow products from scratch. A three-time internal hackathon winner, she prototypes in code as readily as... Read More →
Friday September 11, 2026 15:35 - 16:00 JST Hall 1F
AI systems are increasingly evaluated using benchmarks designed for individual components: Word Error Rate (WER) for speech recognition, MOS for speech synthesis, latency for infrastructure, and task success for agents. Yet users experience none of these components in isolation when using voice agents, they experience conversations.
Voice agents expose the limitations of traditional evaluation more clearly than any other AI system. An agent can achieve state-of-the-art WER, MOS, and latency metrics while still delivering a frustrating user experience.
This talk explores why established metrics are becoming insufficient for real-time AI applications and introduces a framework for evaluating voice agents holistically. We will examine concepts such as semantic understanding versus transcription accuracy, latency distributions versus averages, interruption handling, recovery from errors, and conversation-level success metrics.
Through real-world examples from production voice systems, attendees will learn how to move beyond component benchmarks and begin measuring what ultimately matters: whether an AI system successfully helps a user achieve their goal.
I’m a Developer Relations Engineer at Smallest AI, bridging real-time Voice AI and developer communities with focus on enabling developers to build with open-source orchestrators. Before this, I was a Software Engineer at Mobile Premier League where I built real-time networking... Read More →
Friday September 11, 2026 16:45 - 17:10 JST Hall C
Technical debt lives in code. Cognitive debt lives in your head. Intent debt, Addy Osmani's term for the goals, constraints, and rationale behind a decision that never got written down, lives nowhere. It's the one debt agents can't pay down: an agent drafting a spec fills the gap with a confident guess, and the guess is usually wrong.
This talk covers building context graphs that capture the why: connecting decisions to the document threads and ticket discussions where humans actually made them, then exposing that structure to agents alongside code and data. I'll draw on an open-source toolkit that bootstraps context graphs from existing work artifacts (workspace threads, ticket histories) and the modeling questions that turned out hard: what separates a decision from a discussion, and when capture should run as a background process versus an explicit act.
Vendors now race to pitch "context layers for agents." I'll argue the differentiator isn't collection but connection: rationale linked to the entities, people, and prior choices it affects: queryable, not just retrievable.
You'll leave with patterns for making the why available to every agent that touches your work.
Nyah Macklin is a seasoned researcher and speaker on AI, ML, Ethics, Governance, and Responsibility. Nyah serves as a Sr. Developer Advocate at Neo4j, specializing in context engineering, knowledge graphs, and AI-driven developer tooling where Nyah has built high-impact technical... Read More →
Friday September 11, 2026 17:20 - 17:45 JST Hall C
