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    <id>https://o16g.com/updates</id>
    <title>o16g Updates</title>
    <updated>2026-04-12T11:59:06.284Z</updated>
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    <entry>
        <title type="html"><![CDATA[Update — Apr 12, 2026, 07:58 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-12T07:58:57.607Z</updated>
        <summary type="html"><![CDATA[Sunday, April 12, 2026 · 06:01Z Hardening agents: benchmarks, infra, and the new model economy How We Broke Top AI Agent Benchmarks: And What Comes Next. UC Berkeley researchers build an automated agent that exploits eight major agent benchmarks, exposing systemic vulnerabilities that inflate capability scores. Outcome engineers must treat benchmarks as adversarial surfaces — add red-team evaluation, robust harnesses, and continual validation (Principles 02 & 16)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 12, 2026, 01:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-12T01:01:00.884Z</updated>
        <summary type="html"><![CDATA[Sunday, April 12, 2026 · 00:01Z Agents in the Wild: Security, Sandboxes, and Real-World Rollouts Launch HN: Twill. ai (YC S25) — Delegate to cloud agents, get back PRs runs sandboxed coding agents that build, test, and open PRs, pinging you only for approvals. If you design agentic dev workflows this is a concrete delivery pattern — sandboxed execution lanes with human approval gates mirror Principle 07 and Principle 03]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 11, 2026, 07:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-11T19:01:02.927Z</updated>
        <summary type="html"><![CDATA[Saturday, April 11, 2026 · 18:01Z Agent Ops: Skills, Advisors, Sandboxes, Booking, and Security Launch HN: Twill. ai (YC S25) — Delegate to cloud agents, get back PRs. Twill runs sandboxed coding agents that build, test, and open PRs, pinging you only for approvals]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 11, 2026, 01:58 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-11T13:58:27.341Z</updated>
        <summary type="html"><![CDATA[Saturday, April 11, 2026 · 12:01Z Ship Agents: Skills, Advisors, Security, Infra & Orchestration Launch HN: Twill. ai (YC S25) — Delegate to cloud agents, get back PRs launches sandboxed coding agents that build, test, and open pull requests, alerting humans only for approvals. This gives outcome engineers a concrete pattern for turning autonomous code delivery into auditable artifacts and approval gates — a Build the Island + Gate pattern for safe deployment]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 11, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-11T07:01:01.878Z</updated>
        <summary type="html"><![CDATA[Saturday, April 11, 2026 · 06:01Z Fixing Agent Coordination: Advisors, MCP, Tiger Teams, and Sandboxes AI agents aren’t failing. The coordination layer is failing. An “Event Spine” proposal centralizes ordering, context propagation, and coordination primitives to prevent multi-agent conflicts and scale orchestration]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 11, 2026, 01:58 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-11T01:58:00.110Z</updated>
        <summary type="html"><![CDATA[Saturday, April 11, 2026 · 00:01Z Agent Ops: GPUs, Event Spines, Advisors, Backends, and Tiger Teams SkyPilot Agent Skill: Let Agents Manage Your GPUs releases an Agent Skill that lets AI coding agents launch, manage, and autostop GPU clusters across clouds using natural language. Outcome engineers get a programmatic, observable compute lifecycle for agents — reducing manual ops, improving reproducibility, and turning compute into a shippable artifact (Principles 07/06). The coordination laye...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 10, 2026, 07:57 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-10T19:57:47.199Z</updated>
        <summary type="html"><![CDATA[Friday, April 10, 2026 · 18:02Z Agent infrastructure & production: MCP, GPU skills, orchestration SkyPilot Agent Skill: Let Agents Manage Your GPUs lets AI coding agents launch, manage, and autostop GPU clusters across clouds using natural language. That gives outcome engineers a concrete pattern for delegating infrastructure lifecycle and cost control to agents, turning GPU provisioning into an auditable agent skill (Principle 03). AAIF MCP Dev Summit: Gateways, gRPC, and Observability Signa...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 10, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-10T13:01:00.586Z</updated>
        <summary type="html"><![CDATA[Friday, April 10, 2026 · 12:01Z Outcome Engineering: Payments, On‑Device AI, MCP, GPU Skills, Coordination Visa unveils Intelligent Commerce Connect, a platform for AI-agent payments across card networks. Visa launches a payments bridge that lets AI agents transact across multiple card networks. Outcome engineers now must design secure authorization, audit trails, and fraud controls into agentic payment flows — think Gate and Graph controls around any agent that can move money (Principles 09,...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 10, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-10T07:01:01.668Z</updated>
        <summary type="html"><![CDATA[Friday, April 10, 2026 · 06:02Z Agent Ops: Interop, Managed Agents, Payments, GPU Control, and Multi-Agent Research AAIF MCP Dev Summit: Gateways, gRPC, and Observability Signal Protocol Hardening advances the Model Context Protocol with gateway, gRPC, and observability hardening. This matters because MCP work is becoming the plumbing for production agent fleets—if you build agents at scale you need standardized context channels, hardened gateways, and signal observability to operate safely a...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 10, 2026, 01:56 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-10T01:56:42.257Z</updated>
        <summary type="html"><![CDATA[Friday, April 10, 2026 · 00:02Z Agent Infrastructure: Protocols, Multimodal Retrieval, Payments, Production AAIF MCP Dev Summit: Gateways, gRPC, and Observability Signal Protocol Hardening. The Agentic AI Foundation advances the Model Context Protocol with gateway designs, gRPC support, and observability signal hardening to boost enterprise interoperability, security, and production scaling. Outcome engineers should treat MCP as emergent plumbing for production agents—adopt its gateway and ob...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 9, 2026, 07:56 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-09T19:56:29.974Z</updated>
        <summary type="html"><![CDATA[Thursday, April 9, 2026 · 18:02Z Agents in Production: Skill Artifacts, Managed Runtimes, Offload, and Safety New framework lets AI agents rewrite their own skills without retraining the underlying model. Memento-Skills gives agents an evolving external memory of executable skill artifacts so behaviors evolve without retraining base LLMs. This shifts delivery toward artifact-driven skill management and versioned behavior — think Principle 08 in practice]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 9, 2026, 01:56 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-09T13:56:10.643Z</updated>
        <summary type="html"><![CDATA[Thursday, April 9, 2026 · 12:02Z Agents Into Production: telemetry, skills, managed runtimes, live learning Governance-Aware Agent Telemetry for Closed-Loop Enforcement in Multi-Agent AI Systems describes GAAT, a system that converts multi-agent telemetry into real-time automated policy enforcement. Outcome engineers get a blueprint for closing the observe‑but‑don’t‑act gap—build telemetry that can trigger automated governance and hard checkpoints rather than relying on post hoc audits (Princ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 9, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-09T07:01:00.888Z</updated>
        <summary type="html"><![CDATA[Thursday, April 9, 2026 · 06:01Z Ship Agents: state, skills, memory, and runtime governance Microsoft’s Agent Governance Toolkit targets OWASP top risks for AI agents. Microsoft releases an open-source Agent Governance Toolkit that enforces runtime policies across multi-step agent workflows to mitigate OWASP-class risks. Outcome engineers should treat this as a runtime blueprint for embedding policy checks and telemetry into agent infra rather than bolted-on audits (Principles 10 & 14)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 9, 2026, 01:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-09T01:01:00.788Z</updated>
        <summary type="html"><![CDATA[Thursday, April 9, 2026 · 00:03Z Building Reliable Agents: governance, state, supply‑chain, process, audit Microsoft’s Agent Governance Toolkit targets OWASP top risks for AI agents. Microsoft releases an open-source Agent Governance Toolkit that enforces runtime policies to mitigate OWASP top-10 risks across multi-step AI agent workflows. Outcome engineers must bake runtime policy enforcement and threat modeling into agent platforms to meet enterprise security and compliance — Principle 10 a...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 8, 2026, 07:54 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-08T07:54:32.472Z</updated>
        <summary type="html"><![CDATA[Wednesday, April 8, 2026 · 06:01Z Agent Ops: Scion, 8‑hour LLM, Mythos, Istio, Encoderfile Google Open-Sources Experimental Multi-Agent Orchestration Testbed Scion. Outcome engineers get a practical sandbox for testing identity isolation, credential handling, and workspace hygiene — a direct step toward agent-native infrastructure and Principle 07/09 practices. 1, a 754B MoE open-source model engineered for eight-hour autonomous agentic workloads and 202k-token contexts]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 8, 2026, 01:54 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-08T01:54:10.086Z</updated>
        <summary type="html"><![CDATA[Wednesday, April 8, 2026 · 00:01Z Agent Ops: orchestration, long‑horizon models, security, infra, failures Google Open-Sources Experimental Multi-Agent Orchestration Testbed Scion. Google open-sources Scion, a containerized multi-agent orchestration testbed that isolates agent identities, credentials, and shared workspaces across local and remote compute. Outcome engineers get a reproducible sandbox to validate agent identity, credential handling, and orchestration patterns before production ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 7, 2026, 07:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-07T19:01:00.833Z</updated>
        <summary type="html"><![CDATA[Tuesday, April 7, 2026 · 18:01Z Agents, Context, Memory, Audit: 5 Practical Signals The Anatomy of an Agent Harness. It defines the agent harness as the full orchestration stack—tools, memory, context, and guardrails—and presents MongoDB’s Canvas Framework for productionizing agents. Outcome engineers get a concrete blueprint for turning experiments into stable delivery lanes and for treating harnesses as first-class infrastructure (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 7, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-07T13:01:02.350Z</updated>
        <summary type="html"><![CDATA[Tuesday, April 7, 2026 · 12:02Z Ship Agents: Sandboxes, Harnesses, MCPs, Benchmarks & Second Opinions Launch HN: Freestyle — Sandboxes for AI Coding Agents provides instant, forkable VMs to run and scale tens of thousands of AI coding agents in isolated sandboxes. Sandboxed forks let you run ephemeral agents with deterministic environments, observability, and kill-switches—an essential infra pattern for production agent fleets (Principle 07/09). MCP maintainers from Anthropic, AWS, Microsoft,...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 7, 2026, 07:53 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-07T07:53:10.413Z</updated>
        <summary type="html"><![CDATA[Tuesday, April 7, 2026 · 06:02Z Agent infrastructure: harnesses, sandboxes, MCP, multi-agent tools, tests The Anatomy of an Agent Harness defines the agent harness as the full orchestration stack—tools, memory, context, and guardrails—and presents MongoDB’s Canvas Framework for productionizing agents. It matters because it gives a concrete blueprint for building reliable orchestration and guardrails in production agent systems (Principles 06 & 09). It matters because it solves isolation, repr...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 7, 2026, 01:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-07T01:01:01.552Z</updated>
        <summary type="html"><![CDATA[Tuesday, April 7, 2026 · 00:01Z Agents as Infrastructure: harnesses, MCP, sandboxes, and on-device agents The Anatomy of an Agent Harness defines the agent harness as the full orchestration stack—tools, memory, context, and guardrails—and presents MongoDB’s Canvas Framework for productionizing agents. For outcome engineers this is a practical blueprint for structuring harness layers so agents can act reliably and safely in production (Principles 06 & 09). MCP maintainers from Anthropic, AWS, ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 6, 2026, 07:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-06T19:01:05.128Z</updated>
        <summary type="html"><![CDATA[Monday, April 6, 2026 · 18:04Z Agents, Sandboxes & Edge LLMs: Outcome Engineering Brief Claude, OpenClaw, and the new reality: AI agents are here — and so is the chaos. Outcome engineers must treat agents as infrastructure—prioritize authorization, monitoring, and least-privilege controls (Principles 10, 14, 15). MCP maintainers from Anthropic, AWS, Microsoft, and OpenAI lay out enterprise security roadmap at Dev Summit]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 6, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-06T13:01:01.873Z</updated>
        <summary type="html"><![CDATA[Monday, April 6, 2026 · 12:02Z Agent Infrastructure: IDEs, Browsers, Devices, Chaos, and Architecture Cursor’s $2 billion bet: The IDE is now a fallback, not the default. Cursor 3 replaces the IDE with an agent-first control plane, making editors a fallback and enabling portable cloud-local agent sessions. Outcome engineers should design for agent session portability and orchestration as first-class infra rather than assuming the IDE is the primary surface (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 6, 2026, 07:51 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-06T07:51:31.477Z</updated>
        <summary type="html"><![CDATA[Monday, April 6, 2026 · 06:02Z Agent-first control planes, local Gemma, and LLMOps lessons Cursor’s $2 billion bet: The IDE is now a fallback, not the default introduces Cursor 3 as an agent-first control plane that treats editors as fallbacks and enables portable cloud-local agent sessions. Outcome engineers must design orchestration surfaces and CI for agent sessions to make agents reliable in production (Principle 09). Claude, OpenClaw, and the new reality: AI agents are here — and so is t...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 6, 2026, 01:51 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-06T01:51:17.736Z</updated>
        <summary type="html"><![CDATA[Monday, April 6, 2026 · 00:02Z Agents Take Control: APIs, Local LLMs, and Agent-First Tooling research-llm-apis — 2026-04-04 release. Simon Willison catalogs raw JSON and curl patterns across LLM vendors to redesign LLM abstractions for server-side tool execution. Outcome engineers get a practical map for building vendor-agnostic tool adapters and server-side tool execution layers, making orchestration and context plumbing more predictable (Principles 03, 06)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 5, 2026, 07:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-05T19:01:00.928Z</updated>
        <summary type="html"><![CDATA[Sunday, April 5, 2026 · 18:03Z Agent Ops: orchestration, APIs, wiki memory, and cheap GPU Cursor’s $2 billion bet: The IDE is now a fallback, not the default. Cursor 3 ships an agent-first control plane that treats editors as a fallback and supports portable cloud-local agent sessions. This reframes developer tooling as agent orchestration infrastructure—central for building reliable agentic systems and a practical step toward Principle 09]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 5, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-05T13:01:02.560Z</updated>
        <summary type="html"><![CDATA[Sunday, April 5, 2026 · 12:03Z Agent Stack: APIs, Memory, GPU Slicing, Coding Agents, and Inference Racks Components of a Coding Agent breaks coding agents into six essential components, showing how context, tools, memory, and harnesses make LLMs practical for software work. This gives outcome engineers a checklist for building reliable agent delivery lanes and harnesses that reduce brittleness in production (Principle 06/11). research-llm-apis — 2026-04-04 release catalogs raw JSON and curl ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 5, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-05T07:01:01.164Z</updated>
        <summary type="html"><![CDATA[Sunday, April 5, 2026 · 06:02Z Agent Ops: coding agents, APIs, distillation, interpretability, audits Components of a Coding Agent breaks coding agents into six essential components, showing how context, tools, memory, and harnesses turn LLMs into practical software teammates. Use it as a concise blueprint and checklist when you design agent architectures and map responsibilities between agent, toolchain, and human reviewers. research-llm-apis — 2026-04-04 release catalogs raw JSON and curl p...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 5, 2026, 01:50 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-05T01:50:03.626Z</updated>
        <summary type="html"><![CDATA[Sunday, April 5, 2026 · 00:01Z Agents, Determinism, and Cognitive Risk — Build the Checks The cognitive impact of coding agents argues that coding agents reshape developer cognition, increasing oversight needs and risking long-term cognitive debt without better guardrails. Outcome engineers must treat agentic tools as cognitive infrastructure—design explicit review lanes, audit trails, and team coordination to avoid silent erosion of expertise (Principles 03 & 14). Components of a Coding Agen...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 4, 2026, 07:49 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-04T19:49:45.524Z</updated>
        <summary type="html"><![CDATA[Saturday, April 4, 2026 · 18:03Z From RAG to reproducible agents: five practical reads We replaced RAG with a virtual filesystem for our AI documentation assistant. They replace RAG with a virtual filesystem that lets agents grep, ls, and cat docs instantly, cutting boot time to ~100ms and cost to zero. Outcome engineers get a concrete interface pattern for fast, debuggable context access that simplifies Map and Tech Island work]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 4, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-04T13:01:00.883Z</updated>
        <summary type="html"><![CDATA[Saturday, April 4, 2026 · 12:03Z Outcome Ops: Inference tiers, RAG alternatives, replayable agents, security Google adds Flex and Priority inference tiers to Gemini API for enterprise cost and reliability control. Google introduces Flex and Priority inference tiers on Gemini API to let teams trade cost for latency and availability. Outcome engineers can use these tiers to budget agent SLAs and avoid noisy-neighbor failures in orchestration systems (Principle 12)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 4, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-04T07:01:00.629Z</updated>
        <summary type="html"><![CDATA[Saturday, April 4, 2026 · 06:02Z Agent plumbing, RAG fixes, and inference control for outcome engineers Understanding the risks of OpenClaw. The piece frames OpenClaw as orchestration plumbing, not a standalone cloud, and details how its value and risks hinge on external models, APIs, and distributed trust boundaries. Outcome engineers must treat orchestration layers as fragile trust planes—design explicit provenance, least-privilege interfaces, and governance controls (Principles 09,10)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 4, 2026, 01:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-04T01:01:03.335Z</updated>
        <summary type="html"><![CDATA[Saturday, April 4, 2026 · 00:02Z Outcome Engineering: Local Models, Better Retrieval, Orchestration Risk Arcee’s Trinity-Large-Thinking: U. S. -made 399B open-source model enterprises can download and customize]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 3, 2026, 07:48 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-03T19:48:13.961Z</updated>
        <summary type="html"><![CDATA[Friday, April 3, 2026 · 18:03Z Agents, Data, and Defense: Practical Moves for Outcome Engineers Why pgEdge thinks MCP (not an API) is the right way for AI agents to talk to databases introduces pgEdge’s MCP Server for Postgres, which gives agents schema-aware, secure, low-token connections to Postgres—even in air-gapped deployments. This matters because outcome engineers now have a protocol-first pattern for direct, auditable agent-data access that reduces RAG fragility and reframes connector...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 3, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-03T13:01:00.803Z</updated>
        <summary type="html"><![CDATA[Friday, April 3, 2026 · 12:02Z Agent Infrastructure: Local LLMs, CI, Orchestration, Debt, and Attacks Lemonade by AMD: fast open-source local LLM server for GPU and NPU launches a fast, open-source local LLM server that runs multimodal models on GPUs and NPUs with OpenAI-compatible APIs. This gives outcome engineers a pragmatic route to self-hosted agent infrastructure for lower latency, data locality, and privacy — a Build the Island move that changes deployment and trust boundaries. The Hid...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 3, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-03T07:01:01.529Z</updated>
        <summary type="html"><![CDATA[Friday, April 3, 2026 · 06:03Z Agent Ops: Local LLMs, CI Breaks, Security, and Data Links Lemonade by AMD: fast open-source local LLM server for GPU and NPU delivers a fast, open-source local LLM server that runs multimodal models on GPUs and NPUs with OpenAI-compatible APIs. Outcome engineers can host agent brains on-prem or at the edge with drop-in APIs to cut latency, control data residency, and lower inference costs — a practical enabler for building isolated agent islands and local graph...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 3, 2026, 01:47 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-03T01:47:01.524Z</updated>
        <summary type="html"><![CDATA[Friday, April 3, 2026 · 00:03Z Agent Ops: orchestration, DB protocols, local LLMs, CI, security Cursor launches Cursor 3, an ‘agent-first’ coding product for managing multiple AI agents. Cursor ships an agent-first IDE that runs and coordinates multiple coding agents against OpenAI and Anthropic. Outcome engineers now get a practical orchestration surface for multi-agent workflows—treat agents like services and bake in coordination and observability (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 2, 2026, 07:46 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-02T19:46:19.600Z</updated>
        <summary type="html"><![CDATA[Thursday, April 2, 2026 · 18:03Z Agent ops, validation, and human handoffs — five briefs Real-time dashboard for Claude Code agent teams releases Agents Observe, a live dashboard that captures Claude Code agent event streams, tool calls, subagent hierarchies, and searchable session timelines. Outcome engineers need this kind of observability to make agent behavior, tool usage, and session traces legible for debugging, audits, and iterative context engineering (Principles 06 & 13). Why coding ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 2, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-02T13:01:00.940Z</updated>
        <summary type="html"><![CDATA[Thursday, April 2, 2026 · 12:02Z Agent Ops: Orchestration, Governance, and the New Threats Run multiple agents at once with /fleet in Copilot CLI adds a /fleet command that runs parallel sub-agents to decompose multi-file tasks and synthesize final artifacts. Outcome engineers can use this pattern to scale agentic workspaces and enforce deterministic orchestration of sub-tasks — practical Agentic Coordination in action (Principle 09, Principle 06). Kilo launches KiloClaw for Organizations to ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 2, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-02T07:01:02.379Z</updated>
        <summary type="html"><![CDATA[Thursday, April 2, 2026 · 06:02Z Agent Ops: /fleet, Observability, Governance, NIST & a Double‑Agent Flaw Run multiple agents at once with /fleet in Copilot CLI. GitHub’s Copilot CLI adds a /fleet command that runs parallel sub‑agents to decompose multi‑file tasks and synthesize final artifacts. This turns a single‑agent workflow into a lightweight orchestration primitive you can ship into developer tooling — a practical win for agentic coordination and faster loop times (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 2, 2026, 01:45 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-02T01:45:12.618Z</updated>
        <summary type="html"><![CDATA[Thursday, April 2, 2026 · 00:02Z Agent Ops — orchestration, risk, governance, observability Run multiple agents at once with /fleet in Copilot CLI adds a /fleet command that runs parallel sub-agents to decompose multi-file tasks and synthesize final artifacts. This makes multi-agent orchestration a first-class developer pattern — plan for subtask routing, shared context management, and artifact merging when you design agent pipelines (Principles 09, 06). Vertex AI ‘double agent’ flaw exposes ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 1, 2026, 07:44 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-01T19:44:57.013Z</updated>
        <summary type="html"><![CDATA[Wednesday, April 1, 2026 · 18:02Z Agent Ops: security, standards, orchestration, and testing Run multiple agents at once with /fleet in Copilot CLI. GitHub adds /fleet to Copilot CLI to run parallel sub-agents that decompose multi-file tasks and synthesize final artifacts. Outcome engineers should treat this as a practical pattern for parallel agent decomposition and artifact orchestration — Agentic Coordination in practice (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 1, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-01T13:01:01.737Z</updated>
        <summary type="html"><![CDATA[Wednesday, April 1, 2026 · 12:02Z Agents, Gateways, and Guardrails: Productionizing Agent Workflows Portkey open-sources its AI gateway after processing 2 trillion tokens a day. Portkey open-sources its unified AI Gateway and MCP gateway after processing two trillion tokens daily, enabling self-hosted governance and agent control for production AI. Outcome engineers get a practical gateway for routing, policy enforcement, and telemetry — the kind of Gate and Orchestration control you need to ...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 1, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-01T07:01:02.001Z</updated>
        <summary type="html"><![CDATA[Wednesday, April 1, 2026 · 06:02Z Agent Ops: Context, Gateways, Self‑Hosted Agents & Dev PromptQL Turns Teams and Slack Messages into Secure Context for AI Agents. PromptQL turns Slack and Teams conversations into a secure canonical shared wiki that gives agents real-time, queryable context and automatically actionable work assignments. Outcome engineers can use this virtual data layer to reduce brittle prompt engineering and provide agents dependable, auditable context]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Apr 1, 2026, 01:43 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-04-01T01:43:45.664Z</updated>
        <summary type="html"><![CDATA[Wednesday, April 1, 2026 · 00:02Z Agent Ops: Gateways, Context, and Agents for Production Outcomes Portkey open-sources its AI gateway after processing 2 trillion tokens a day announces Portkey’s unified AI Gateway going open-source after massive production traffic. Outcome engineers get a ready-made control plane for self-hosted governance and policy enforcement across agent fleets — a practical foundation for agent orchestration and enterprise-level controls (Principle 09). PromptQL Turns T...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Mar 31, 2026, 07:43 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-03-31T19:43:29.416Z</updated>
        <summary type="html"><![CDATA[Tuesday, March 31, 2026 · 18:03Z Agent Ops: local hosts, canonical context, and post‑training tools Coasts — Containerized Hosts for Agents launches isolated, containerized development hosts that boot N reproducible workspaces for agent-driven workflows without any hosted service. Outcome engineers can run many reproducible agent sandboxes locally for iteration, observability, and secure experimentation — a practical way to build the island before you commit to cloud infra (Principle 07). Pro...]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Mar 31, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-03-31T13:01:02.133Z</updated>
        <summary type="html"><![CDATA[Tuesday, March 31, 2026 · 12:02Z Agent Ops: shipping, hosting, identity, and multi-model workflows How Stripe built “minions”: AI coding agents that ship 1,300 PRs per week. Stripe builds “minions” that convert Slack reactions into cloud-backed AI agents and ship roughly 1,300 reviewable PRs per week. This shows an agent-as-delivery-lane pattern where orchestration, review gates, and CI integration scale output — a blueprint for Principles 09 and 03 in practice]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Mar 31, 2026, 07:01 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-03-31T07:01:01.362Z</updated>
        <summary type="html"><![CDATA[Tuesday, March 31, 2026 · 06:02Z Agent Ops: OSes, Plugins, Code Review, and Production Minions Sycamore raises $65M to let enterprises build, deploy, and monitor AI agents. Sycamore is building an enterprise agent operating system and just closed a $65M raise to scale deployment and monitoring for agent fleets. Outcome engineers should treat agent OSes as foundational infra—they centralize orchestration, observability, and policy enforcement for production agents (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Mar 31, 2026, 01:42 AM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-03-31T01:42:26.972Z</updated>
        <summary type="html"><![CDATA[Tuesday, March 31, 2026 · 00:02Z Agent Ops: Orchestration, OS, and production tooling What is OpenClaw. OpenClaw turns chatty AI into agentic automation that executes real-world workflows end-to-end. If you build outcome systems, OpenClaw shows how an agent runtime converts intent into action and where orchestration, tool integration, and permissions must live (Principle 09)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Mar 30, 2026, 07:42 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-03-30T19:42:10.253Z</updated>
        <summary type="html"><![CDATA[Monday, March 30, 2026 · 18:02Z Agent Infrastructure: OS, Registries & Production Agents Sycamore raises $65M to let enterprises build, deploy, and monitor AI agents. The startup is building an enterprise agent operating system that centralizes deployment, monitoring, and policy controls. Outcome engineers should evaluate agent OSes as the platform layer that enforces lifecycle, observability, and gating for production agents (Principle 09/15)]]></summary>
    </entry>
    <entry>
        <title type="html"><![CDATA[Update — Mar 30, 2026, 01:01 PM]]></title>
        <id>https://o16g.com/updates</id>
        <link href="https://o16g.com/updates"/>
        <updated>2026-03-30T13:01:01.575Z</updated>
        <summary type="html"><![CDATA[Monday, March 30, 2026 · 12:02Z Agents, Context, and Memory — practical wins for outcome engineers What if AI doesn’t need more RAM but better math. — How TurboQuant compresses the KV cache describes TurboQuant, a KV-cache compression technique that slashes memory for long-context LLM inference without degrading accuracy. This changes deployment tradeoffs for outcome engineers: you can keep larger conversational state and longer histories in-memory, reducing the need for brittle external cont...]]></summary>
    </entry>
</feed>