Agentic Engineering Loops, Graphs, and What Comes Next
An agent is just a loop. Everything that matters is what you build around it.
02
act 0 — where we are
The Whole Talk in One Line
ralph.sh
$while :; do cat PROMPT.md | claude-code ; done
Geoffrey Huntley, "Ralph Wiggum as a software engineer", July 2025 — archival: quoted as published; the CLI binary has since changed, do not modernize
03
act 0 — where we are
How Far Agents Have Come
9 s 2020
→
4 min 2023
→
40 min late 2024
→
hours 2025–26
~12 h at 50% reliability roughly a working day — at coin-flip odds
~70 min at 80% reliability about an hour — if you need 4-in-5
Horizon = human-time of tasks done at 50% success — NOT autonomous runtime (MIT Tech Review: the "most misunderstood graph in AI"). Doubling ~7 months; ~89 days for models ≥2024.
METR TH1.1, verified 2026-08-28 (Opus 4.6: 718.8 min at 50%, wide CI); MIT Technology Review 2026-02
04
act 0 — where we are
What Is an Agent
2024
"Agents are typically just LLMs using tools based on environmental feedback in a loop."
Anthropic, 2024
2025
"Agents are models using tools in a loop"
Hannah Moran / Simon Willison, 2025
2025, honest version
"An AI agent is an LLM wrecking its environment in a loop."
Solomon Hykes
Every definition got shorter. None dropped the word loop.
05
Act I — The Loop
06
act 1 — the loop
The Agentic Loop
gather context
→
take action
→
verify
→
repeat ↺
"Loops that write code need loops that check it."
Anthropic, "Getting started with loops" (2026)
Maker ≠ checker
"The agent that wrote the code isn't the one grading it." — Osmani
Fresh eyes win
Devin Review: avg 2 bugs/PR, ~58% severevendor-internal — works best with zero shared context between coder and reviewer.
This maker/checker shape returns in Act 2 as the evaluator-optimizer workflow pattern.
■ 1.6% AI decision logic — the rest of Claude Code is the systems around the loop. Osmani independently lands on 10% model / 90% harness(Osmani, "The New Software Lifecycle").
"A decent model with a great harness beats a great model with a bad harness."
Addy Osmani, "Agent Harness Engineering" (2026)
Ralph
zero harness — a while-loop and a prompt file. It works.
Claude Code
maximal harness — 98.4% of the code is scaffolding around the same loop. It works better.
Rules, skills, and hooks are harness components — the previous talk covered how to build them.
arXiv 2604.14228, "Dive into Claude Code" (2026); Osmani, "The New Software Lifecycle" (2026)
08
act 1 — the loop
Verification Earns Autonomy
Autonomy is earned by verification — not granted by the task name.
The generation–verification loop
Fast generation + tight verification = reliability. Small diffs; loosen the leash only as verified trust grows.
Karpathy, "Software Is Changing (Again)" (2025)
One slider isn't enough
orchestration ↑agency →L0 assistL2 scoped delegationL5 managed by exception
Osmani, "Agentic Autonomy Levels" (2026)
How fast will we know we're wrong?
How cleanly can we undo?
What would prove we're right?
09
act 1 — the loop
Four Kinds of Loops
Turn-based
trigger: your message stop: every turn — you verify each one
Goal-based
trigger:/goal + explicit completion criteria stop: evaluator confirms, or turn cap
Time-based
trigger: the clock — /loop 5m, /schedule stop: timer or schedule ends
Proactive
trigger: an event — no human live stop: guardrails and escalation rules
Loop = heartbeat, goal = hands. What loops don't buy you: vague goals and taste.
Anthropic, "Getting started with loops" (2026); independently confirmed by Osmani, "Practical Loop Engineering" (2026)
10
act 1 — the loop
Inner, Middle, Outer
outer — market feedbackweeks
middle — engineer steeringhours
inner — the agentminutes
gather → act → verify → repeat
"A product moves at the pace of its slowest loop."
Andrew Ng, The Batch (2026)
11
act 1 — the loop
The Bottleneck Migrated
+98% PRs merged
+91% review time
+154% PR size
Faros AI/DORA, via Osmani, "The 80% Problem in Agentic Coding" (2026)
"We got faster cars, but the roads got more congested."
Addy Osmani
Jevons paradox: "Teams are working more, not less — AI has a Jevons paradox quality beyond token consumption."
Linear, How teams build (2026)
12
act 1 — the loop
Own the Outer Loop
"Agents run the inner loop. Engineers own the outer loop."
Addy Osmani, "Own the Outer Loop" (2026)
INNER · agent capability — checks run
— evidence crosses →
OUTER · engineer agency — a human decides
Quality checks produce evidence
→
Verdict the production decision
→
Answerability "if someone asks, I can explain why"
13
act 1 — the loop
The Loop That Learns
plan
→
work
→
review
→
compound
↺
"The first three steps produce a feature. The fourth step produces a system that builds features better each time."
Kieran Klaassen, Every — Compound Engineering (2026)
Extract, don't repeat
The middle runs without you plan→work→review should be boring
Document the thinking the reasoning compounds, not the code
Always ≥50% extraction
14
act 1 — the loop
The Judgment Debate
"Implementation is mostly solved. Judgment isn't."
Every, Compound Engineering
vs
"You delegate the task, not the judgment."
Addy Osmani, Practical Loop Engineering
15
Act II — The Graph
16
act 2 — the graph
Workflows vs Agents
The canonical axis: who decides the next step — code or the model?
Workflows
"LLMs and tools orchestrated through predefined code paths."
code decides the next step
Agents
"LLMs dynamically direct their own processes and tool usage."
the model decides the next step
"The most successful implementations weren't using complex frameworks... simple, composable patterns."
Anthropic, "Building Effective Agents" (2024)
17
act 2 — the graph
Four Workflow Patterns
Prompt chaining
step→gate→step
Routing
router
↙↓↘
ABC
Parallelization
split
↙↓↘
§§§
↘↓↙
merge / vote
Orchestrator-workers
w1⇄orchestrator⇄w2
⇅
w3
The fifth pattern — evaluator-optimizer — you already met in Act 1: it's the maker/checker loop.
The SDLC becomes a loop of committed artifacts — an incident re-enters as a new intent.md, with no human in the invocation path.
Sierra, "Release governance: guardrails for agents at scale" (2026); Anthropic, "The AI-Native SDLC Playbook" (2026)
28
act 3 — the fleet
The Adoption Barbell
"Adoption is binary, skill is a spectrum." (Varick, 2026)
5–10%power users
~20% shallow use
~70%never use it
Varick — client rollouts
5–10% power / 20% shallow / 70% never; 10% of seats burn 90% of the tokensvendor anecdote, corroborated pattern
Varick Agents, "AI Adoption is a Myth" (2026)
Ronacher — bimodal poll
44% write <10% of code manually vs 44% still >90% by hand
Armin Ronacher poll (5,000 devs), via Osmani, "The 80% Problem" (2026)
Science — peer-reviewed
Gains concentrate in senior devs; juniors show no measurable benefit
Science (2025), AI-assisted code study
88% of orgs adopted AI vs 6% seeing >5% EBIT impact (McKinsey 2025) · 95% of pilots: no measurable P&L impact (MIT NANDA)
McKinsey State of AI (2025); MIT NANDA, The GenAI Divide (2025)
For the 70%: the loop goes to the work, not the worker.
29
act 3 — the fleet
What Comes Next
spec-first write the spec before the code
→
spec-anchored the spec stays the reference
→
spec-as-source AWS Kiro · GitHub Spec Kit · Tessl
Self-improving harnesses
Chimera: an agent framework built by agents (~144k lines). Huntley's software factory: found a bug, fixed it, deployed, verified — autonomous auto-heal (Jan 2026).
The economics tension
Altman: 2–3-person billion-dollar companies. Hassabis: 3–4× productivity should mean building more, not cutting people.
Chimera — yad.codes, "Building Chimera" (2026); Huntley auto-heal (2026-01); Altman via TBPN (2026-02); Hassabis via Wired (2026)
"What if the models don't stop getting good?"
Geoffrey Huntley
30
act 3 — the fleet
Judgment Stays Above
write code where we started
→
design the loop act I
→
design the graph act II
→
own the judgment act III
"Human judgment doesn't leave the software factory. It relocates."
Addy Osmani (2026)
"The loop keeps running. Human judgement stays above it."