2026/07/22
The Venue Is a Sensor: Physical Space Is About to Out-Data the Internet
Research · 8 MIN READ
By 2029, Gartner forecasts that AI agents operating in physical environments will generate ten times more data than all digital use cases combined. For an experiential director, this is not a prediction - it is an operational shift. The venue is becoming a data surface, and designing an activation now means specifying what the space emits, what agents see, and how they respond.
2026/07/20
The Case Against Multi-Agent, From Someone Who Runs Twelve
Research · 9 MIN READ
Enterprise agentic AI pilots fail at scale - not because the models are weak, but because teams build orchestration before they define evaluation criteria. Multi-agent systems are only justified when sub-tasks are genuinely independent. An audit of twelve specialised agents, and the test that determines which ones survive it.
2026/07/17
Designing for Mostly Fine: Why Graceful Degradation Is a Brand Value
Research · 7 MIN READ
Graceful degradation is not the fallback plan you build when the real plan fails. It is the design position that makes a live brand activation, or a fleet of AI agents, worth trusting at all. The difference between a brand activation that survives a failure and one that collapses under it is not luck - it is architecture.
2026/07/15
LLM Seeding: How to Make Physical Experiences Discoverable in AI Search
Research · 8 MIN READ
Physical brand experiences - activations, flagship openings, live installations - generate no machine-readable record unless you build one deliberately. LLM seeding is the practice of constructing structured, externally-cited digital assets so that AI search engines can retrieve, parse, and cite your brand when composing an answer. The mechanics differ from SEO, the timing is front-loaded, and for experiential brands specifically, the seeding window closes when the installation is struck. This is the practice of Generative Engine Optimisation (GEO) applied to the physical experience category.
2026/07/10
The Haunted House Audit: How to Find and Decommission Ghost Agents
Building a Governed Multi-Agent Fleet · 10 MIN READ
Most multi-agent systems are not haunted by failures you can see. They are haunted by agents you forgot to retire - undocumented, unowned, and still running on credentials that were never rotated. This article gives you a concrete checklist to find them and a lifecycle template to stop them forming again.
2026/07/08
Defining Agent Skills Once, Running Them Anywhere
Earned Layers · 9 MIN READ
When an agent skill is defined in a provider-specific schema, it can't move. The Agent Skills standard - with Atlassian, Figma, Notion, Canva, Stripe, and Zapier building out a partner skills directory, and Microsoft and OpenAI as early ecosystem contributors - proposes a different model: define the skill once, compile it to wherever it needs to run. This article documents that pattern in Diana's fleet, and what portability makes possible.
2026/07/03
Telemetry Before the Failure: What I Monitor Across Twelve Agents
Research · 14 MIN READ
Production multi-agent systems do not fail like traditional software. They drift - producing output that looks plausible long after it has stopped being correct. This article documents the seven failure surfaces that show up in a running twelve-agent fleet, and the monitoring layer Diana built to catch them before they cascade.
2026/07/01
The Apprenticeship Gap
Agentic Infrastructure Notes · 11 MIN READ
Private AI surfaces create two simultaneous failures: the organisation stops accumulating the pattern knowledge embedded in its people's AI interactions, and individuals produce work that has never been seen by anyone with the institutional context to recognise what matters. Both failures share one cause — the private surface — and one fix.
2026/06/30
The Control Plane Problem - Why Agent Governance Is the Real Bottleneck
Agentic Infrastructure Notes · 10 MIN READ
Most teams building multi-agent systems can describe what their agents do. Very few can describe what happens when an agent does something wrong. That gap - between build and operation, between capability and control - is the governance problem nobody is shipping a clean solution to yet.
2026/06/28
Your Working Style Shouldn't Live in One App
Research · 8 MIN READ
Most practitioners who use AI seriously have solved the memory problem - the agent knows the context, the projects, the decisions. What shows up next is a different problem: procedural debt. The agent may know what you're working on and still not know how you work. This piece is an attempt to name that gap, and what can actually be done about it.
2026/06/25
Your Agent's Memory Is a Contract, Not a Database
Research · 13 MIN READ
Most agent hallucinations are not model failures - they are contract failures. The agent assumed something about what it would find in memory. Nobody wrote that assumption down. This article names the four things a retrieval contract must specify, and walks through the four-tier memory architecture I built before I had a name for any of it.
2026/06/24
The Maintenance Is the Work
Research · 9 MIN READ
Building an agent is the easy part. The harness around it - what it reads, what it can touch, what keeps it honest - has to keep up with two things moving simultaneously: the world it operates in, and the model running inside it. Almost nobody talks about what that actually requires.
2026/06/22
Verification Is the Primitive
Research · 10 MIN READ
Every workflow improvement in the AI era reduces to the same primitive: making private judgment survive contact with a second reader. The medium changes - a human board, a public channel, an adversarial model, a structured log - but the underlying problem does not. This piece names the primitive and shows four implementations of it in a running fleet.
2026/06/11
The Brief Is Changing - Again
Research · 12 MIN READ
The marketing brief has always been written for one reader: the marketer who will execute it. That was a complete assumption when the output's reader was also human. It is no longer complete. There is now a second reader between the brief's output and the buyer - an AI interpretation layer that does not respond to tone, brand personality, or emotional register. A brief written for only one of these readers will produce the wrong output. Not because the marketer failed. Because the spec was wrong from the start.
2026/06/09
From Prompts to Specifications - How Twelve Agents Forced a Structural Shift
Research · 11 MIN READ
In late June, a drafting batch produced five content pieces where agents had fabricated first-person anecdotes, presenting invented stories as lived experience. The briefs asked for "a lived encounter" without providing one, so the agents confabulated. Reid's voice-check passed them through - it checks register and vocabulary, not factual truth. That incident revealed the absence of a verification step independent of the agents producing output. This article walks what a spec layer addressing that gap looks like in a running production system, and names what it still does not solve.
2026/05/29
Agent Memory Architecture - Shared vs Isolated
Research · 6 MIN READ
When agents operate across multiple projects with different needs, they need persistent memory - but not all memory should be persistent in the same way. Shared memory and isolated memory are architecture decisions with distinct failure modes. This is how the fleet chose.
2026/05/28
The Brief Is Changing
Research · 9 MIN READ
When the interface dissolves - when intent flows directly from the user through an AI agent that assembles the experience rather than presenting a fixed navigation structure - the traditional brief stops working. The specification that matters is no longer what the user sees. It's what the system understands and executes. This is not loss. This is the validation of the working principles this practice is built on.
2026/05/26
The Memory Bundle for a GEO Audit
Research · 10 MIN READ
On every GEO audit run, an agent receives nine pieces of information-exactly nine, fetched deterministically, not retrieved by similarity. Here's why that structural choice beats the RAG pattern competitors use, and what the deterministic-bundle architecture demonstrates about auditable AI infrastructure at scale.
2026/05/24
Seven Questions Before Your Agent Ships
Research · 10 MIN READ
Production agents fail in seven distinct ways. Most teams audit for one or two and ship anyway. This is how to name all seven control points before your agent goes live - and what closing the gaps actually requires.
2026/05/21
We Built Our Own Devin Before Devin Was Worth $25B
Research · 9 MIN READ
Cognition's $25B valuation prices the vision of autonomous coding agents. What running that architecture in production for months reveals is that the market is pricing execution, not specification. This article examines what the valuation gets right, what it underweights, and what operating at scale teaches about the infrastructure layer that determines whether agentic systems actually work.
2026/05/20
The Model Routing Decision - Cost + Fit Over Benchmarks
Research · 7 MIN READ
Benchmarks answer a question production systems do not ask. The real question is which model for which task at what cost - and that question has a routing answer, not a ranking answer. This article walks the decision through Diana's actual thirteen-agent fleet, names the three criteria that govern model assignment, and uses new evidence from Microsoft and NVIDIA to confirm what operational routing already shows.
2026/05/19
Singapore Wrote a Governance Framework for What I Build Every Day
Research · 9 MIN READ
When Singapore's IMDA published what it calls the world's first governance framework for agentic AI, it named four pillars: risk bounding, human accountability, technical controls, end-user responsibility. I had already built all of it - not because I read the framework, but because the failure modes it describes are structurally inevitable. Governance from operational necessity and governance from first principles arrive at the same place. That convergence is the finding.
2026/05/17
When Agents Render the Interface
Research · 11 MIN READ
Agents can now return rendered UI components, not just text. Google's A2UI and CopilotKit's AG-UI make this technically possible. But rendering a component is different from designing one - the interface is no longer a finite deliverable, it's a behaviour. The gap is governance: what is the agent authorised to render, in which contexts, with what constraints?
2026/05/14
The Ceiling Isn't the Model - It's the Data
Research · 12 MIN READ
The conversation about agentic AI centres on model capability upgrades. In production, the ceiling is not model choice - it is context availability. When twelve agents operate across eight repositories without a shared persistence layer, the failures are not intelligence gaps. They are infrastructure gaps.
2026/05/13
What Google's Agent Platform Reveals About Governance
Research · 6 MIN READ
In late April 2026, Google announced the Gemini Enterprise Agent Platform - a production stack for authoring, routing, scaling, and securing agents at enterprise scale. The announcement is not about new AI capabilities. It is about governance infrastructure. This article reads the platform as a governance stack rather than a tooling stack, maps how it solves the same failure modes practitioners have been solving by hand for two years, and names the specification layers that infrastructure cannot automate.
2026/05/07
Guardrails vs. Governance: What Karpathy's CLAUDE.md Looks Like at Scale
Research · 14 MIN READ
Karpathy's four rules for single-agent coding and my governance system for twelve agents converge on the same structural problem - but scale exposes failure modes his ruleset was never built to catch. The distinction between guardrails (rules that prevent mistakes) and governance (systems that make assumptions legible across agents) is not about scope creep; it is about the singular meta-rule that generates both: every correction requiring manual intervention must become a durable rule, with the reasoning attached.
2026/05/05
The Right Model for the Right Agent
Research · 13 MIN READ
A practitioner framework for assigning different AI models to different agent roles in a multi-agent workflow - the three-question decision model, the actual assignments across 12 agents, the override mechanism that keeps tiering flexible, and the observability layer that proves whether it works.
2026/05/04
The Dark Factory Is Already Running
Research · 10 MIN READ
I run a 12-agent system across six repositories without human monitoring of day-to-day operations. The factory runs lights-off because the specification layer is explicit and complete. This essay explains what specification means at autonomous scale, how it differs from monitoring and observability, and why it has to be written before the factory starts.
2026/05/01
When Something Earns SKILL.md Status
Research · 4 MIN READ
This article examines the criteria that justify promoting a procedure to SKILL.md status - and argues that one criterion, the gate, separates skills from well-written rules. Most procedures that earn repetition, shape, and trigger clarity still don't need formal specification.
2026/04/29
Skill Architecture as the Next Frame After Prompt Engineering
Research · 11 MIN READ
As agents operate across multiple projects with varying needs, the single-prompt model breaks down. Skill Retrieval Augmentation frames the architecture that's already necessary at scale - but the centralised model misses the prior question: what load profile does this capability have? The answer determines where it lives.
2026/04/24
What Routines Actually Is
Research · 3 MIN READ
Anthropic's Routines solves the scheduling problem, not the orchestration problem. It replaces manual session management with a cloud scheduler - which is a concrete win for running nightly audits - but leaves the coordination layer, knowledge persistence, and operational discipline entirely to you.
2026/04/22
What I Built Before Karpathy Named It
Research · 8 MIN READ
In April 2026, Andrej Karpathy published a note on "LLM-Wiki" - a system where AI agents maintain a centralised knowledge base. This article documents the same system, built months earlier across six production repositories and twelve agents, for the simplest possible reason: agents kept losing continuity between sessions, and nobody had finished the tools to solve it yet.
2026/04/20
Dark Code: What Ships When Nobody's Looking
Research · 12 MIN READ
What runs in production that nobody fully understands - and the three-layer framework built across six repos to eliminate it. Spec-before-code, behavioral manifests, and a comprehension gate that treats "what fails silently?" as a required deliverable, not a review step.
2026/04/13
A Playbook for Multi-Project AI Teams
Research · 8 MIN READ
A step-by-step practitioner guide for setting up AI agent governance from scratch -- the three-file foundation, the folder structure, the cross-project coordination layer, the five failure modes to watch for, and the health metrics that tell you whether your system is working or quietly degrading.
2026/04/12
Designing Rules for AI Agents
Research · 6 MIN READ
Every rule you write for an AI agent has a cost. At 3,000 tokens of instructions, model performance starts to degrade - not from context limits, but from cognitive load. This article covers the information architecture behind rules that actually work: inheritance patterns, override declarations, what to cut, and why "write clean code" is worse than writing nothing at all.
2026/04/11
Scaling an Agentic Workflow
Research · 6 MIN READ
The system from Part 1 works at one project; this documents what breaks at fourteen - rule drift, planning chaos, decision fragmentation - and the three-layer governance cascade modelled on CSS specificity that fixes it without touching agent logic. Global defaults, project-specific conventions, and scoped rule files via glob patterns. No model upgrades. No changes to the agents themselves. What it proves: governance coherence is a structure problem, not a capability problem.
2026/04/05
Building an Agentic Workflow
Research · 4 MIN READ
How Diana Ismail's 12-agent system stays coherent across 6 repos without losing context or duplicating work. This article documents the three-file architecture - CLAUDE.md hierarchy, manifest conventions, and cross-project state synchronisation - that lets Claude Code scale from one-agent experiments to a production fleet. What breaks at 14 projects, and how the system rebuilds without touching agent logic.