Ten irreducible truths. If you disagree with these, the rest will not help you.
A1
AI is the building layer, not a feature. The agent wraps deterministic tools; tools do not wrap the agent.
A2
The model is a commodity; the moat is everything around it. Data, workflow, evals, distribution. Never the prompt.
A3
Context engineering is the core discipline. Most agent failures are context failures, not reasoning failures.
A4
Compounding requires a closed loop. Sense, decide, act, evaluate, improve. Only the closed loop compounds.
A5
Legibility precedes intelligence. If it was not recorded, it did not happen to your AI.
A6
The constraint shifts from headcount towards inference — directionally, and contested. A direction of travel, not a settled law.
A7
Verticals win on depth. Depth = proprietary data + codified workflow + evals, accumulated through deployment.
A8
Evaluation is the steering wheel. Without evals you are driving blind at speed.
A9
Code is ephemeral; context is permanent. Regenerate code as models improve; the durable asset is what you know.
A10
Start with one named user and one bulletproof loop. Everything else is premature.
02
The concepts form an architecture, not a list. Build from the bottom up. Click a layer to expand.
▲ build upward — each layer rests on the one below ▲
1 · AI as the building layerIf removing the AI leaves a working product, you built a co-pilot. The shadow: the “horseless carriage” anti-pattern (Pete Koomen).
2 · Agent-native propertiesParity, granularity, composability, emergent capability, self-improvement (Dan Shipper).
3 · Machine-readable interfacesBuild for agents: APIs, MCPs, CLIs — not human-first GUIs.
4 · LLM-land vs code-landJudgment in the LLM; deterministic actions in code. Confusing them is the No.1 failure.
5 · Execution → ideation shiftWhen building is near-free, “what to build” becomes the bottleneck (a16z).
6 · The self-improving loopSense → decide → act → evaluate → improve, continuously. See §03.
7 · Context engineeringCurate the finite context window; progressive disclosure, just-in-time retrieval. Beware “context rot”.
8 · Thin harness, fat skillsReuse the harness; put all intelligence in markdown skills (SKILL.md, progressive disclosure).
9 · Legibility — record everythingConversations, tickets, calls, decisions. Cannot be retrofitted.
10 · Company brain / world modelTwo queryable models: how the company works + everything about customers.
11 · Skill self-improvementFeed usage transcripts back as metaprompts; the skill surpasses any individual.
12 · Evals as the steering wheelMeasure consistency across runs. Single-run metrics massively overstate reliability.
13 · Domain-first verticalsOwn a vertical end-to-end. Sierra, Harvey, Decagon, Hippocratic.
14 · Data + workflow moatNot the model, not the prompt. Better models make the app layer more capable, not thinner.
15 · Forward-deployed engineeringEmbed in the customer; extract tacit knowledge; build evals; merge reusable features back.
16 · Burn tokens, not headcountDirectional, contested. Numbers are real; formal metrics were refuted and rolled back.
17 · Flat, egalitarian, trust-by-defaultEveryone gets the infrastructure; conversations visible. A startup-stage edge.
18 · Outcome-based pricingCharge per resolution, not per seat (Sierra). Forces eval discipline.
19 · The named-user gate (Phase 0)PR-FAQ + 3 falsifiable pillars + one named real user + pre-mortem + kill criteria. No name, no build.
20 · Tokenmaxxing (with discipline)Spend tokens on high-leverage work — research, evals, hard reasoning. Track cost-per-outcome.
03
This loop lives inside Layer 2. Its five motions each map to one architectural layer. When all five run with minimal human intervention, the system improves with every cycle.
1
Sense · sensor layer
Read the world: customer messages, tickets, cancellations, telemetry, code changes.
2
Decide · policy layer
Rules for autonomy: what the agent may do alone, what needs approval, what must be logged.
3
Act · tool layer
Execute deterministic real actions through tools. Reversible & audited where it matters.
4
Evaluate · quality gate
Grounding check, safety, brand/policy. Plus production sampling for the side effects evals miss.
5
Improve · learning
Detect what failed; propose skill updates; feed back into the sensor layer. The nightly cycle.
Proof it is real: Cognition’s Devin reportedly became up to 4× faster and 2× more resource-efficient through deployment, lifting merged-PR rate from ~34% to ~67% — a loop that turned on itself.
04
Each step is a prerequisite for the next. Parameterise it: choose a vertical V, a named user U, a core workflow W.
WEEK 0 · GATE
PR-FAQ, three falsifiable pillars, one named user U who confirms they would pay, pre-mortem, kill criteria.
→ Layer 5 discipline
WEEKS 1–2
One database. Record everything from day one. Append-only event log. Cannot be retrofitted.
→ Layer 2 · concept 9
WEEKS 2–4
Choose one harness; do not build your own. Write workflow W as a SKILL.md. Judgment in skill, actions in code.
→ Layer 2 · 7, 8
WEEKS 3–5
Define autonomy boundaries. Mark every action reversible or not. Reversible + low-stakes can be autonomous.
→ loop · decide
WEEKS 5–6
Stand up evals before scaling usage. Grounding, safety, brand. Add production sampling. Measure across runs.
→ Layer 2 · 12
WEEKS 6–8
Nightly cycle reads transcripts, proposes skill improvements. Measure resolution, escalation, satisfaction.
→ loop · improve
MONTHS 2–3
Queryable world models over everything recorded: vector store + event log. Prune for context rot.
→ Layer 2 · 10
MONTHS 4–6
Deepen data + workflow moat. Adjacent workflows on the same substrate, outcome pricing, FDE for high-value verticals.
→ Layer 3 · 13–15
05
Some concepts are entry tickets, some are durable advantages, some are powerful levers that misfire if misapplied.
Do these or you are AI-assisted, not AI-first
Compounding, hard to copy
High leverage, apply with discipline
06
Where most implementations fail. Each is a real failure mode with a concrete mitigation.
E1Thin-wrapper trap
Next model release commoditises you. Fix: moat must be owned data + workflow + evals from deployment, never the prompt.
E2Eval blind spot
Evals only catch what you thought to measure. Fix: production sampling + adversarial evals; monitor downstream effects.
E3Runaway loop
A loop compounds errors as fast as wins. Fix: human gate on high-stakes; reversibility; kill switch; real policy layer.
E4Token-cost explosion
Tokenmaxxing everything burns cash. Fix: only high-leverage tasks; track cost-per-outcome; per-workflow budgets.
E5Legibility vs privacy
“Record everything” hits GDPR/PII. Fix: consent receipts, data minimisation, PII vaults; record process, not raw data.
E6Over-automation
Staff feel replaced; bad autonomous calls. Fix: humans at the edges; approval thresholds; audit trails; reversibility.
E7Model lock-in
One lab’s model; they raise prices or deprecate. Fix: provider-agnostic abstraction; evals catch swap regressions.
E8Flat-org limit
Trust-by-default breaks at scale / regulated. Fix: treat as stage-specific; formalise selectively as you grow.
E9FDE becomes consulting
Bespoke one-offs never merge back. Fix: 70%+ of FDE code in main repo by month 12; one reusable feature per engagement.
E10Building before a named user
Cheaper execution makes it more tempting. Fix: the Phase 0 gate. No name, no build.
E11“What to build” thrash
Execution is cheap; teams build everything. Fix: tight loops with real users; exploration over more pipelines.
E12Context rot
World model accumulates stale, contradictory data. Fix: decay, contradiction detection, periodic distillation — prune as well as add.
07
The categories you assemble, with public examples. Pick one per category and resist rebuilding the commodity layers.
08
Built with adversarial verification: every major claim was challenged, and five popular claims were killed. Share these caveats alongside the guide — they separate a credible reference from hype.
Time-sensitivity: nearly every source dates from Dec 2025 – May 2026 in a fast-moving field. Re-verify before betting heavily.
Primary sources and named case studies. Method: 5-angle web deep-research (24 sources, 115 claims, 25 adversarially verified — 20 confirmed, 5 killed), plus practitioner search, 1 June 2026.
Compiled 2026-06-01, last reviewed 2026-06-02 · evidence-graded and adversarially verified · every example is a public company.
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