Token Maxxing Is Over and Here Is What Comes Next
The era of 'token maxxing' is over. To prove AI's ROI, focus on headcount saved, not token spend. Use 'Sonnet maxxing' and structured meta harnesses to keep human engineers in the loop.
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LLM Orchestration
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Managed Deep Agents - Quickstart This guide walks through scaffolding and deploying Managed Deep Agents using the MDA CLI. Key steps include defining instructions in `instructions.mmd`, local testing with `MDA dev`, and syncing context to the Context Hub before final deployment.Build context
Managed Deep Agents - Skills Skills package task-specific procedures and context into reusable directories. You can define them in markdown files, and they are picked up automatically by the agent. Building Agents Is Trivial Now, Context Is the Next Frontier — Jeff Ng, Unblocked Building agents has become trivial due to cloud primitives, but missing institutional context remains the biggest failure point. A Context Engine solves this by synthesizing knowledge from docs, code, and tickets, preventing outages. Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean Model routing argues that there is no single best LLM. By using a purpose-built Mixture of Experts model, developers can dynamically select the optimal AI model per request based on cost, latency, and task type.Recent signals
Token Maxxing Is Over and Here Is What Comes Next The era of 'token maxxing' is over. To prove AI's ROI, focus on headcount saved, not token spend. Use 'Sonnet maxxing' and structured meta harnesses to keep human engineers in the loop. Smarter Models Killed the Harness. Here's What's Left — Jediah Katz, SpaceXAI Is the agent harness dead? Jediah Katz explains that while models are absorbing coding skills via the data flywheel, the harness remains critical for infrastructure, security, and unlocking new, non-coding frontiers. Stop Telling Agents How — Dan Barrett, Raymond Weitekamp & Jose Montes de Oca, OpenProse OpenProse defines multi-agent workflows as declarative contracts in plain markdown. This framework enables event-driven, auditable automation for tasks like PR reviews and quant model documentation.
Smarter Models Killed the Harness. Here's What's Left — Jediah Katz, SpaceXAI
Is the agent harness dead? Jediah Katz explains that while models are absorbing coding skills via the data flywheel, the harness remains critical for infrastructure, security, and unlocking new, non-coding frontiers.
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Stop Telling Agents How — Dan Barrett, Raymond Weitekamp & Jose Montes de Oca, OpenProse
OpenProse defines multi-agent workflows as declarative contracts in plain markdown. This framework enables event-driven, auditable automation for tasks like PR reviews and quant model documentation.
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Agents in Orbit: Workflows, Not Autopilot — Eduardo Simon & Javier Casal, Flywire
Flywire's Orbit platform integrates LLMs into deterministic workflows using YAML definitions, ensuring AI-driven automation maintains strict control and compliance within existing CI/CD infrastructure.
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An AI Future Without the Lock-In — Remy Guercio, Tailscale
To escape AI lock-in, modern AI deployments must be modular. An AI gateway centralizes identity, manages budgets, and decouples the LLM, data connectors (MCP), and interfaces, ensuring maximum choice and ROI.
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Managed Deep Agents | Deploying mission critical agents to production
Managed Deep Agents simplifies deploying mission-critical AI agents. It bundles the Deep Agents harness with managed LangSmith infrastructure, allowing developers to define, test, and deploy agents using the mda CLI.
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Why Your AI Agents Can't Talk to Each Other (Yet) — Vlad Luzin, BAND
Connecting agents is a distributed systems problem requiring solutions for ordered transport, persistence, and runtime binding. BAND introduces an interaction layer to solve the single-agent bottleneck.
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Lifestyles of the AI-Native — Nick Nisi & Zack Proser, WorkOS
Learn to operate a 'fleet of agents' instead of babysitting single sessions. We cover Goals, Loops, Hooks, and Git worktrees to build fully autonomous, verifiable development workflows.
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Agent Memory Is Solved. Agent Learning Isn't. — Karthik Ranganathan, Yugabyte
Agent memory is solved, but shared learning across agents is not. The solution requires an agent-native persistence layer (Meko) to promote private memories into governed, shared data packs.
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Interrupt NYC: Opening Keynote
LangChain outlines the shift to 'owning your intelligence' by building domain-specific agents. The platform provides Managed Deep Agents, Jev decision models, and LangSmith Engine v2 for robust governance and production readiness.
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The $600 PR: Where Software Factory ROI Really Comes From
The ROI in an AI software factory comes from the system surrounding the agent—context, verification, and feedback loops—not the agent itself. Learn how to measure the full cost of a PR.
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Schedules for Managed Deep Agents: Cron jobs, prompts, and Slack delivery
Learn how to use Schedules for Managed Deep Agents to automate recurring tasks. Configure cron jobs to generate weekly reports from Salesforce and deliver them automatically to Slack via LangSmith.
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Ship a GitHub PR From a Slack Message with Managed Deep Agents
Learn how to build Patch, an agent that converts Slack messages into working GitHub PRs. This walkthrough details using Managed Deep Agents to integrate Slack, GitHub, and a sandbox with minimal code.
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5 Voice Agent Failure Modes You'll Hit in Week One — Venky B, Plivo
Voice AI agents often fail in production due to latency, brittle data collection, and poor LLM-to-TTS handoffs. The talk outlines solutions using structured data modeling and normalization layers.
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Agents Without Code: Skills, YAML, and Filesystems Replaced Python — Philipp Schmid, Google DeepMind
Agents are evolving from complex Python loops to declarative, file-based definitions. The new architecture uses a secure sandbox and server-side state management, allowing developers to focus on instructions and capabilities rather than orchestration code.
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Teaching agents to pay — Anna Spysz, Stripe
Anna Spysz discusses 'agent-to-commerce,' showing how agents use structured data, the Universal Commerce Protocol (UCP), and shared payment tokens to execute complex purchases.
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Preferences Over Benchmarks: Model Routing — Archana Kamath & Tyler Gillam, DigitalOcean
Model routing argues that there is no single best LLM. By using a purpose-built Mixture of Experts model, developers can dynamically select the optimal AI model per request based on cost, latency, and task type.
Read article
Building Agents Is Trivial Now, Context Is the Next Frontier — Jeff Ng, Unblocked
Building agents has become trivial due to cloud primitives, but missing institutional context remains the biggest failure point. A Context Engine solves this by synthesizing knowledge from docs, code, and tickets, preventing outages.
Read article
Managed Deep Agents - Skills
Skills package task-specific procedures and context into reusable directories. You can define them in markdown files, and they are picked up automatically by the agent.
Read article
Managed Deep Agents - Quickstart
This guide walks through scaffolding and deploying Managed Deep Agents using the MDA CLI. Key steps include defining instructions in `instructions.mmd`, local testing with `MDA dev`, and syncing context to the Context Hub before final deployment.
Read article