Topic
State Serialization
13 episodes
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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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Post Deterministic Distributed Systems:A New Foundation for Trustworthy Autonomous Infrastructure
Puck and Kore dig into a paper proposing Post-Deterministic Distributed Systems, or P D D S, as a way to reason about infrastructure where deterministic services, stochastic models, agents, and humans all participate. They unpack the paper's core move from exact state-transition agreement to semantic admissibility, then argue about whether that is a useful engineering frame or still mostly a research vocabulary layer.
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Agent frameworks vs the AI Backend — AgentField Docs
Justy and Cody unpack AgentField's claim that agent frameworks are for authoring behavior, while an AI Backend is for running agents as production services.
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Infrastructure patterns for agentic applications
Justy and Cody unpack why naive HTTP‑wrapped AI agents break in production and walk through three infrastructure patterns—web‑queue‑worker, workflow engines, and a hybrid approach—highlighting idempotency, compensation, and real‑world product impact on teams building long‑running agents.
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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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Graph Based Agentic AI with LangGraph: Workflow Pathways for Long Running Stateful Business Processes
Cooper and Miles dig into a practitioner paper on LangGraph as a control-plane for long-running business workflows, not a benchmark toy. They focus on the three recipes in the paper—SQL repair loops, evidence-gated RAG, and human-in-the-loop policy review—and on when a graph is actually worth the extra structure.
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Overview: Durable Execution
We’re finally slowing down and unpacking durable execution from the ground up, because it keeps showing up in our conversations and it actually deserves the full treatment. We’re using the book-with-bookmarks idea to make the mechanics of checkpoints, retries, and recovery click without hand-waving.
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Overview: Append Only Logging
We’re finally making append-only logging click, because it keeps sneaking into the stuff we cover and we keep assuming everybody sees the mechanism already. We walk from the basic idea to why it gives AI systems a durable, auditable trail, and where that trade-off starts to bite.
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Overview: State Serialization
We finally slow down and explain state serialization from the ground up: what it is, why it matters, and how it lets an AI pause, resume, and hand off work without losing the thread. We keep it in our own voice and stay close to the actual mechanism, because state serialization is one of those ideas we keep circling for a reason.
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To Every Agent Its Own Database
Jessica and Cathy dig into Joe Reis’s argument that agentic data systems should flip the warehouse model: give each agent its own embedded analytical engine, then exchange immutable slices peer-to-peer instead of routing everything through one shared platform. They focus on why that matters for concurrency, freshness, and trust, and where the idea still needs real control-plane machinery.
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Kwc2SSaP0y
Buzz argues that the workspace for software teams should treat people, agents, messages, workflows, and code as parts of one shared system. Cooper likes the product shape, while Miles argues the hard part is whether signed events and self-hosting produce usable coordination rather than another fragmented collaboration stack.
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How to Implement a Unified Memory From Scratch
Jessica and Cathy dig into a new post that walks through building a unified agent memory from scratch using knowledge graphs and MongoDB, unpacking what it actually takes to wire memory into a real agent harness. They tease apart where the post’s blueprint shines, where it overreaches, and who on earth should actually roll their own instead of reaching for an off-the-shelf tool.
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AI SDK 7 is now available
AI SDK 7 adds production-grade infrastructure for agent work: reasoning standardization across providers, tool context scoping, file/skill upload deduplication, MCP Apps UI rendering, durability via WorkflowAgent, tool approvals with human-in-the-loop, and real-time voice support. The core argument is that agents aren't just bigger models—they're systems that need control surfaces, state management, and approval gates to run reliably in production. Laura sees this as the toolkit finally catching up to what teams are actually building; Harper sees solid engineering but flags that the real bottleneck is still harness design, not SDK features.