Insights
Notes from the work, not the hype.
Practical thinking from the engineers and operators replacing manual work with systems that actually ship in production.
Your RAG Cites the Wrong Policy Because Your Docs Disagree
Retrieval works, the corpus doesn't. How outdated, duplicated, and contradictory docs make agents confidently cite the wrong policy — and how to surface the conflict.
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Define the Acceptance Test Before the Build, Not After
Why we write a concrete, measurable acceptance test with clients before an 8-12 week AI build starts — and how it kills projects that shouldn't exist.
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The Agent That Passed Every Eval and Rotted Anyway
Agents ship passing evals, then quietly degrade over weeks as prompts get tweaked and vendor models update. How to build CI-style regression gates that catch it.
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Design the Refusal Boundary Before Your Agent Deletes a Record
How to define the actions a production AI agent must never take alone — and enforce it with tool permissions and validation gates, not prompt pleading.
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The Trace You Wish You Had After an Agent Fails in Prod
The span-level tracing and attributes we instrument on every agent before go-live — so a failed answer takes minutes to localise, not a day.
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Human-in-the-Loop That Doesn't Train Reviewers to Rubber-Stamp
Approve-every-action gating trains reviewers to click yes. How to gate on uncertainty, blast radius, and reversibility instead — with a decision table.
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The Data Access Layer Is Where Agent Projects Actually Stall
Enterprise agent projects don't stall on model choice or prompts. They stall on stale ETL, row-level permissions, and no clean API. A CTO's de-risking checklist.
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Document Chunking for RAG: What We Dropped and What We Ship
The chunking strategies we tried and abandoned across messy enterprise corpora — and the structure-aware, metadata-rich approach we default to now.
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Prompts Are Artifacts, Not Dashboard Config: How We Version Them
How we version, eval-gate, canary, and roll back prompts in production AI systems — and why editable dashboard prompts silently degrade quality across a fleet.
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Your RAG Isn't Hallucinating — It Never Saw the Answer
Teams grade answer quality while ignoring whether the right passage was even retrieved. How to build a retrieval-only eval and why it comes first.
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Five RAG Failure Modes We Keep Finding in Audits
A candid field guide to the five RAG failure modes we keep diagnosing in client audits — symptom, real root cause, and the cheapest fix for each.
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How We Stress-Test an AI Agent Before It Ships
The harness we use to break an agent before production does: adversarial inputs, failure injection, tail latency, cost per resolved task, and injection probes.
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How We Cut an Agent's Token Bill 60% Without Losing Eval Points
An anonymised case study: where an agent's tokens actually went, the changes that cut spend ~60%, and the measurement discipline that proved quality held.
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How We Shipped a Support Agent That Deflected 40% of Tickets
An anonymised case study of a customer-support agent that deflected ~40% of inbound tickets without escalation-rage — the failures, the fixes, and the deflection ceiling.
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Catching Agent Failure in Production Before Your Users Do
How to detect AI agent failure in production before users notice — drift detection, online monitoring, confidence-based fallbacks, and the telemetry that makes it work.
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Workflow or Agent? A Decision Framework for Enterprise AI
Most enterprise tasks handed to us as 'agent' projects ship better as constrained workflows. Concrete criteria for deciding when to give a model agency — and when to take it away.
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Why Your Agent Makes 40 Tool Calls to Do a 6-Call Job
Production agents balloon their tool-call counts with redundant retrievals and second-guessing loops. How to instrument, budget, and detect thrashing.
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Why LLM Confidence Scores Lie — and Break Your Human Gate
LLM self-reported confidence and logprobs are badly calibrated. Here's how that breaks human-in-the-loop gating in production agents — and the cheap fixes that work.
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Golden Eval Datasets Rot: How to Keep Yours Honest
Golden eval sets silently rot within months. How we source real edge cases, version evals alongside prompts, and refresh without invalidating history.
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The Agent That 10x'd Traffic and Blew Its Budget
An anonymised case study of an AI agent that worked in pilot but blew the budget at scale. Where the cost went, the fixes, and the before/after numbers.
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The Model Writes the Bug and the Confidence
AI-generated code ships exploitable flaws with total confidence. Why security review is the one step you can't delegate to the tool that caused the problem.
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Treat AI Agents Like Untrusted Insiders, Not Magic
DeepMind's AI Control Roadmap signals the shift from hoping models stay aligned to securing agents like insider threats. Here's the executive mental model.
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The Eval Harness Is the Deliverable, Not the Agent
For agentic systems, the eval harness is what lets you ship with confidence and iterate safely. Here's what goes into a real one — and why teams pay for skipping it.
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Why Your RAG Demo Works and Production Doesn't
The specific failure modes that kill RAG systems after the demo — retrieval drift, chunk boundaries, stale indexes — and how to catch them with evals first.
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Build vs Buy: AI Agents for Enterprise
A decision framework for CTOs and Heads of AI in 2026. Four paths — Build, Buy, Partner, Wait — with honest costs, timelines, and failure modes.
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Why Most Enterprise AI Pilots Die in Month 4
The month-four death has remarkably consistent causes. None of them are technical. Four organisational failure modes that kill AI pilots.
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How Anglo American Cut Fleet Downtime 40% with Predictive Maintenance
Case study: 100+ vehicles, 2 years, 40% downtime reduction. What we built, why it worked, and the patterns worth stealing.
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The Anatomy of a 6-Week Enterprise AI Project
There is a specific shape that enterprise AI projects take when they actually ship. Here's the week-by-week breakdown.
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Buy vs Build vs Fractional: A Decision Framework for Enterprise AI
The question is almost never framed correctly. A framework for deciding how to stand up AI capability — honest about where each option breaks.
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AI Automation for Enterprise: What Actually Works in 2026
Most enterprise AI projects fail. Here's what separates the ones that ship from the ones that stall — based on what we've seen across 6 industries.
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Why More Companies Are Hiring a Fractional Head of AI
You need senior AI leadership but aren't ready for a full-time hire. Here's how the fractional model works and when it makes sense.
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