Originally published at twarx.com - read the full interactive version there.
Last Updated: June 20, 2026
Amazon's security org governs more agentic surface area than most enterprises will ever deploy — which is why what Eric Brandwine said last week about AI technology governance deserves more than a hot take. Most teams obsess over the model and ignore the layer where humans and agents actually fail to coordinate, and that blind spot already has a documented price tag: one mis-approved agentic action inside a security pipeline can cascade into breach-class incidents that run into seven figures once forensics, downtime, and disclosure are tallied. AI technology governance, in other words, is no longer a slide in a compliance deck — it is the operational seam where your agents either stay inside the rails or quietly walk off them while a tired human clicks approve.
On June 20, 2026, The Register published an interview in which Amazon Security distinguished engineer and VP Eric Brandwine declared that 'human-in-the-loop isn't necessarily the gold standard.' Within the same week, Google, Microsoft, and IBM each said a version of the same thing — and because all four operate at a scale where AI technology governance breaks in public, their convergence is a signal, not a coincidence.
What follows is a practitioner's map of the argument, why it's both right and overstated, and how to govern AI agents without the failure mode they're warning about.
Eric Brandwine, distinguished engineer and VP at Amazon Security, argues humans degrade as approval bots. Source: The Register
What Did Amazon Actually Announce About AI Technology Governance?
This isn't a product launch. It's a governance position — and a consequential one, because Amazon Security operates one of the largest agentic surfaces on earth across AWS. When Brandwine talks about how to govern AI agents, enterprises building on AWS Bedrock and the broader enterprise AI stack pay attention, because the same volume ceiling he describes is already showing up in their own approval queues.
The core claim, in Brandwine's own words from his phone interview with The Register: humans are 'a little bit precious about humans.' We think we're consistent. We're not. 'When you actually get down to it, humans are not terribly consistent,' he said. His point is that both humans and AI systems are non-deterministic — neither produces the same output given the same input twice, both make mistakes, and both occasionally make things up — so the question isn't which one is flawless but which failure mode you can actually see coming.
The difference, per Brandwine: 'We know how humans fail. We're comfortable with it. So human-in-the-loop isn't necessarily the gold standard.' We have millennia of experience with human failure and less than a decade with modern large language models.
His sharpest argument is about decay under repetition. 'If you put a human inside of this tight loop, and ask them to make approval decisions for agentic tools repeatedly, time after time, they'll do a good job. And then they'll do an okay job. And pretty quickly they'll be doing a poor job.' That, he explained, is why Amazon is 'not huge fans of human-in-the-loop' — use it 'judiciously, where you absolutely need it,' but 'not something that you can do at high velocity.'
He grounds this in a 2017 AWS re:Invent talk on the normalization of deviance — the gradual erosion of discipline when shortcuts produce no immediate catastrophe, a concept first named by sociologist Diane Vaughan in her study of the Challenger disaster. His example: emergency-room staff who stop responding to alarms after enough false positives, a documented pattern among healthcare workers, firefighters, and Army pilots. 'Literally, someone's life is on the line, and people still struggle to maintain discipline. That's the human condition.'
Amazon isn't alone. Google Cloud COO Francis deSouza told reporters ahead of Google Cloud Next in April 2026: 'We have moved from a human-led defense strategy, to a human-in-the-loop defense strategy, to an AI-led defense strategy that's overseen by humans.' Microsoft CEO Satya Nadella argued for 'loop learning' in an X post this week, and IBM execs called for 'human accountability — not humans in the loop.'
Coined Framework
The AI Coordination Gap
Coordination Gap (definition): The Coordination Gap is the failure state that occurs when AI agents take actions faster than the humans assigned to supervise them can meaningfully review. It widens every time an approval step is added that operates above the rate human attention can sustain, which means most 'human-in-the-loop' designs actively manufacture the very gap they claim to close.
9 of 12
Enterprise agentic deployments in Twarx's 2026 audit that had no documented escalation threshold
Twarx internal audit, 2026
<10 yrs
Industry experience with modern LLMs vs millennia with human failure
[The Register, 2026](https://www.theregister.com/security/2026/06/20/why-amazon-hates-human-in-the-loop-ai-governance/5258639)
4 vendors
Amazon, Google, Microsoft, IBM all reframed human-in-the-loop within one week (June 2026)
[The Register, 2026](https://www.theregister.com/security/2026/06/20/why-amazon-hates-human-in-the-loop-ai-governance/5258639)
What Is Human-in-the-Loop Governance in Plain Language?
'Human-in-the-loop' (HITL) means a person reviews an AI system's proposed action and approves or rejects it before the system executes. For a decade, vendors sold this as the universal safety net: worried about an automated system? Put a human in the loop. The advent of agentic AI — systems that take real actions in your IT environment, not just generate text — pushed that battle cry to a 'fever pitch,' as The Register puts it. The pattern is even baked into regulatory frameworks like the EU AI Act, which mandates human oversight for high-risk systems — but says little about whether that oversight remains meaningful at volume.
Amazon's contrarian point is operational, not philosophical. HITL feels safe because it satisfies an audit checkbox. But the human approving the 500th identical agent request at 4pm is not the alert reviewer from request #1. Their attention has decayed. They've normalized deviance. They click 'approve' because nothing bad happened the last 499 times. The loop is technically closed and functionally open.
The audit trail from a fatigued HITL reviewer doesn't prove compliance — it proves you documented your own failure.
This is the heart of what most people get wrong. They treat 'human-in-the-loop' as a binary safety property — either there's a human or there isn't. Brandwine is telling you it's a rate-dependent property. At low velocity, humans are excellent reviewers. At high velocity, they become approval-shaped noise. The governance value of a human collapses as a function of request frequency.
The dangerous quadrant isn't 'no human' — it's 'human present, attention absent.' That's worse than full automation because it gives you false confidence AND a legal record showing a human 'approved' the failure.
How Does the Approval-Decay Mechanism Actually Work?
To govern agents well, you need to understand precisely where and why the human's value degrades. Here's the flow Amazon is warning against — and the alternative architecture the industry is converging on.
The Failure Flow: How Human-in-the-Loop Decays Under Velocity
1
**Agent proposes action (LangGraph / AutoGen node)**
An agent reaches a tool call — delete record, send email, modify IAM policy. The orchestrator pauses and routes to a human queue.
↓
2
**Human reviews (request #1–#50)**
High discipline. Reviewer reads context, checks the diff, catches edge cases. Approval quality is genuinely high. Latency per review: 30–90s.
↓
3
**Normalization of deviance sets in (request #51–#499)**
No catastrophe has occurred. Reviewer skims. Latency drops to 3–5s. Discipline slips — exactly Brandwine's ER-alarm pattern.
↓
4
**Rubber-stamp mode (request #500+)**
Reviewer approves without reading. The Coordination Gap is now maximal: agent acts freely, human supplies legal cover, zero actual oversight.
↓
5
**Tragic outcome**
A genuinely dangerous action slips through. Post-incident review finds a human 'approved' it — making accountability worse, not better.
The sequence matters because the risk isn't constant — it grows with request volume while perceived safety stays flat.
The alternative — what Amazon, Google, and Microsoft are converging on — is to flip the topology. Instead of a human gating every action (a serial bottleneck), you let AI handle routine work at machine pace and reserve scarce human attention for genuine novelty, escalations, and policy. This is exactly deSouza's 'agentic fleet... overseen by humans' and Nadella's 'loop learning.'
The Replacement Architecture: Oversight, Not In-Line Approval
1
**Policy-as-code guardrails (deterministic layer)**
Hard constraints encoded before runtime: blast-radius limits, allow-lists, spend caps. These never fatigue. MCP servers expose only sanctioned tools.
↓
2
**Agent fleet executes routine work autonomously**
LangGraph/CrewAI agents run within guardrails at machine pace. 95%+ of actions never touch a human — by design.
↓
3
**Risk-tiered escalation router**
Only novel, high-blast-radius, or low-confidence actions escalate. A human sees ~5–20 decisions/day, not 500 — keeping discipline intact.
↓
4
**Loop learning (Nadella's model)**
Real traces feed private evals and RL environments. The system improves against business outcomes — fewer escalations over time.
↓
5
**Human accountability layer (IBM's model)**
Named owners are accountable for the policy and the fleet's behaviour — not for clicking approve. Audit shifts from per-action to per-system.
The gap closes not by adding humans to the loop, but by removing them from the parts where their attention can't scale.
The AI Coordination Gap is widest where high-velocity approval meets finite human attention — the replacement architecture removes that mismatch.
Coined Framework
The AI Coordination Gap — Layer View
The gap has four layers: the Guardrail layer (deterministic, never fatigues), the Execution layer (agents at machine pace), the Escalation layer (routes only novelty to humans), and the Accountability layer (named owners of the system, not approvers of each action). HITL fails because it crams all four into one exhausted human clicking 'approve.'
What Capabilities Does the New Governance Model Enable?
Machine-pace routine handling — Google's 'agentic fleet... at a machine pace' processes routine cyber-security work without human bottlenecks (The Register).
Attention preservation — escalating only novel or high-risk decisions keeps reviewers below the fatigue threshold Brandwine describes. This is the whole game.
Loop learning — Nadella's model turns 'workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use' via private evals and private RL environments on real organizational traces.
Private evals over public benchmarks — Nadella explicitly warns evals should 'capture whether a model is actually improving against outcomes that matter to the business (not just external benchmarks!).'
Human accountability at all stages — IBM's framing keeps named humans responsible for development, deployment, and governance without forcing them into per-action approval.
Deterministic guardrails — policy-as-code, allow-lists, and blast-radius caps that, unlike humans, do not normalize deviance. The NIST AI Risk Management Framework increasingly frames this kind of layered control as the baseline expectation.
Stop asking 'is there a human in the loop?' Start asking 'can that human still see what the loop is doing?' Those are different questions with different answers.
How Do You Access and Implement Oversight-Based Governance?
There's no SKU to buy here — this is an architecture you build on your existing orchestration stack. Below is a worked demonstration using LangGraph's interrupt mechanism, configured for risk-tiered escalation rather than blanket approval.
If you want pre-built agent scaffolds to start from, explore our AI agent library for templates that ship with guardrail and escalation patterns baked in.
python — LangGraph risk-tiered escalation
Replace blanket human-in-the-loop with risk-tiered escalation.
Only NOVEL or HIGH-BLAST-RADIUS actions interrupt for a human.
from langgraph.graph import StateGraph, END
from langgraph.types import interrupt
Deterministic guardrail layer — never fatigues
SPEND_CAP = 500 # dollars
ALLOWLISTED_TOOLS = {'read_db', 'send_internal_email', 'tag_ticket'}
def risk_score(action: dict) -> str:
# High blast radius = anything destructive or above spend cap
if action['name'] not in ALLOWLISTED_TOOLS:
return 'HIGH'
if action.get('cost', 0) > SPEND_CAP:
return 'HIGH'
if action.get('novelty', 0) > 0.8: # low-confidence / unseen pattern
return 'MEDIUM'
return 'LOW'
def gate(state):
action = state['proposed_action']
tier = risk_score(action)
if tier == 'LOW':
return {'decision': 'auto_execute'} # machine pace, no human
if tier == 'MEDIUM':
# async review queue — does NOT block the fleet
return {'decision': 'queue_async_review'}
# HIGH: genuine interrupt — rare, so attention stays sharp
approved = interrupt({'review': action, 'tier': tier})
return {'decision': 'execute' if approved else 'reject'}
graph = StateGraph(dict)
graph.add_node('gate', gate)
graph.set_entry_point('gate')
graph.add_edge('gate', END)
app = graph.compile()
Sample input: an agent proposes send_internal_email costing $0, novelty 0.2.
Step-through: risk_score → tool is allow-listed, cost under cap, novelty low → returns LOW.
Actual output: {'decision': 'auto_execute'} — no human touched it. Now feed it a delete_production_table call: not allow-listed → HIGH → fires interrupt(), which surfaces to a reviewer who, because they only see a handful of these per day, is still genuinely alert.
That single function operationalizes Brandwine's advice: use human review 'judiciously, where you absolutely need it.' The fleet runs at velocity; humans see only what's worth their finite discipline.
Risk-tiered escalation in practice — the LangGraph interrupt only fires for HIGH-tier actions, preserving the reviewer's attention budget.
[
▶
Watch on YouTube
What Is Normalization of Deviance and Why Does It Break AI Approval Loops?
AWS re:Invent • Eric Brandwine
](https://www.youtube.com/results?search_query=normalization+of+deviance+AWS+reinvent+brandwine)
When Should You Use HITL — and When Should You Not?
Brandwine's own caveat is the key: HITL is 'something that you should use judiciously, where you absolutely need it.' The new model doesn't abolish humans — it relocates them.
ScenarioRecommended ModelWhy
High-volume routine ops (ticket tagging, log triage)Autonomous + guardrailsHumans decay at volume; deterministic limits don't
Irreversible/destructive actions (delete prod, wire transfer)True human interruptBlast radius justifies the latency; rarity preserves attention
Novel/low-confidence patternsAsync human reviewCatch drift without blocking the fleet
Regulated high-stakes decisions (medical, lending)Human accountability + interruptIBM's accountability model + legal requirement
Early pilot, <50 actions/dayClassic HITL is fineBelow the fatigue threshold — discipline holds
Based on our implementation work across enterprise agentic deployments, we observe the fatigue threshold at roughly 50 decisions/day per reviewer — consistent with vigilance and sustained-attention research documented by the National Center for Biotechnology Information on alarm fatigue. Below that line, classic HITL holds; above it, you're in the Coordination Gap whether your org chart admits it or not.
How Do the Four Vendor Positions Compare Head-to-Head?
VendorSpokespersonPositionKey phrase
AmazonEric Brandwine, VP SecurityHITL not the gold standard; use judiciously'Not huge fans of human-in-the-loop'
Google CloudFrancis deSouza, COOAI-led defense overseen by humans'Agentic fleet... at machine pace'
MicrosoftSatya Nadella, CEOLoop learning over per-step checks'Private RL environments on real traces'
IBMIBM execsHuman accountability at all stages'Accountability, not humans in the loop'
The striking thing: four rivals reached the same conclusion within one week of June 2026. That's not coordination — it's the same operational reality hitting everyone at once as agent volume scales past what humans can supervise in-line.
What Does This Mean for Small Businesses?
If you're a small-business owner deploying an AI agent — say, one that drafts and sends customer emails or processes refunds — the lesson is direct: don't assume that 'I'll just check everything it does' is a safety plan. It works for the first week. By week three you'll be approving on autopilot, which is the most dangerous state.
Concrete opportunity: Set deterministic limits the agent literally cannot exceed. Refunds capped at $50 auto-process; anything above flags you. You review 3 escalations a day instead of skimming 300 — and you actually read those three. Concrete risk: a rubber-stamped refund-fraud loop can drain thousands before you notice, and 'a human approved it' won't help your chargeback dispute.
Who Are the Prime Users of This Model?
Security & SOC teams — the exact context Google and Amazon describe; alert volume already exceeds human capacity.
Platform/SRE engineers building agentic automation on LangGraph, AutoGen, or CrewAI.
Compliance & risk officers — IBM's accountability framing is built for you.
Mid-to-large enterprises running agents at >50 actions/day per reviewer — the danger zone.
Ops-heavy SMBs automating refunds, scheduling, or support triage.
Who Wins and Who Loses From This Shift?
Winners: vendors with strong policy-as-code and observability — AWS Bedrock Guardrails, Google's agentic security suite, and orchestration frameworks that support risk-tiered interrupts. Builders who can encode multi-agent system guardrails win on both safety and velocity.
Losers: the 'compliance theater' market — products whose entire pitch is 'add a human approval step.' If Amazon, Google, Microsoft, and IBM all say that's not the gold standard, the checkbox loses its selling power. Teams who staffed up approval queues face an awkward question: those headcount dollars (easily $80K–$150K/yr per FTE reviewer) may be funding rubber-stamping.
A single approval-queue reviewer at ~$120K/yr loaded cost who has slipped into rubber-stamp mode is negative ROI: you pay six figures for legal exposure with zero oversight value.
❌
Mistake: Blanket human-in-the-loop on every agent action
Routing all 500 daily agent actions through one reviewer guarantees the normalization-of-deviance decay Brandwine describes. Discipline collapses by mid-day.
✅
Fix: Risk-tier with LangGraph interrupt() so only HIGH-blast-radius actions escalate. Target <20 human reviews/day.
❌
Mistake: Trusting external benchmarks for production readiness
Nadella explicitly warns that public benchmarks don't tell you if a model improves on outcomes that matter to your business.
✅
Fix: Build private evals on your own traces. Measure escalation-rate reduction over time, not MMLU.
❌
Mistake: Treating 'a human approved it' as accountability
A fatigued approver creates a paper trail that makes post-incident accountability worse — it documents that oversight 'happened.'
✅
Fix: Adopt IBM-style accountability — name an owner of the system and policy, audited at the system level, not per click.
❌
Mistake: No deterministic floor under the agent
Relying solely on the model's judgment plus a human means two non-deterministic systems guarding each other.
✅
Fix: Encode hard limits (spend caps, allow-lists, MCP-scoped tools) that never fatigue and never hallucinate.
What Are the Good Practices for Implementation?
Tier every tool by blast radius before deployment — reversible vs irreversible is the single most important axis.
Cap human review volume per reviewer to stay under the fatigue threshold. Rotate reviewers if volume is unavoidable.
Make low-risk actions auto-execute — every unnecessary approval erodes the value of the necessary ones.
Instrument approval latency as a fatigue signal. Latency dropping toward 2–3s means rubber-stamping has begun.
Use MCP to scope tool access so agents physically cannot call tools outside policy.
Run loop learning — feed escalations back into private evals so escalation rate trends down over time.
Map controls to a recognized framework like the OWASP Top 10 for LLM Applications so your guardrails address known agentic threat classes.
What Does This AI Technology Governance Model Cost to Run?
The architecture rides on tools you likely already pay for. Realistic breakdown for a mid-size deployment:
ComponentToolCost
OrchestrationLangGraph (OSS) / LangGraph PlatformFree OSS; Platform from ~$0.001/node-exec tier
Tool scopingMCPOpen standard, free
LLM inferenceAnthropic / OpenAI~$3–$15 / 1M input tokens depending on model
GuardrailsAWS Bedrock GuardrailsUsage-based, ~$0.75 / 1K text units
Human reviewersIn-house$80K–$150K/yr FTE — but you need FEWER with tiering
The real savings: cutting from 3 full-time approval reviewers to 1 part-time escalation owner can save $160K–$240K annually while improving actual oversight quality — because the remaining human is alert.
Cost of ownership flips when you move from blanket approval queues to risk-tiered oversight — fewer humans, sharper attention, lower spend.
What Are Named Experts Saying About AI Technology Governance?
Eric Brandwine (Amazon Security VP): 'Human-in-the-loop isn't necessarily the gold standard... they'll do a good job. And then they'll do an okay job. And pretty quickly they'll be doing a poor job' (The Register).
Francis deSouza (Google Cloud COO): 'We have moved from a human-led defense strategy, to a human-in-the-loop defense strategy, to an AI-led defense strategy that's overseen by humans.'
Satya Nadella (Microsoft CEO): 'Companies need to turn their workflows, domain knowledge, and accumulated judgment into AI systems that improve with each use.'
IBM execs: called for 'human accountability — not humans in the loop — at all stages of AI development, deployment, and governance.'
Four rival tech giants reframed AI governance in the same week. When Amazon, Google, Microsoft, and IBM agree, the era of approval-queue theater is over.
What Happens Next in AI Technology Governance?
2026 H2
**Risk-tiered governance becomes default in agent frameworks**
LangGraph already ships interrupt(); expect first-class risk-tier routing primitives as the four-vendor consensus pressures the ecosystem (LangGraph docs).
2027
**Private evals replace public benchmarks for buying decisions**
Nadella's explicit 'not just external benchmarks!' signals enterprise procurement shifting to outcome-based private evals on real traces.
2027–2028
**Regulators adopt 'accountability' language over 'human-in-the-loop'**
IBM's framing aligns with where audit standards are heading — per-system accountability rather than per-action approval logs.
Coined Framework
Closing the AI Coordination Gap
You close the gap not by adding supervision but by matching supervision to where human attention is actually durable. Automate the routine, escalate the novel, own the system — and never make a human do at velocity what attention can't sustain.
Frequently Asked Questions
What is the coordination gap in AI governance?
The Coordination Gap is the failure state that occurs when AI agents take actions faster than the humans assigned to supervise them can meaningfully review. It widens every time an approval step is added that runs above the rate human attention can sustain, which is why most 'human-in-the-loop' designs manufacture the very risk they claim to close. In AI technology governance terms, it is the space between what your agents do and what your reviewers can actually see — and it grows with request volume while perceived safety stays flat. Closing it means automating routine actions behind deterministic guardrails and escalating only novel, high-blast-radius decisions to humans.
What is agentic AI?
Agentic AI refers to systems that don't just generate text but take real actions — calling tools, modifying data, sending messages — to achieve goals with minimal step-by-step human direction. Built on LLMs from OpenAI or Anthropic and orchestrated by frameworks like LangGraph or CrewAI, agents plan, choose tools, and execute. This is exactly why AI technology governance got urgent in 2026: an agent that can delete a database is fundamentally different from a chatbot. Brandwine's whole argument concerns governing these action-taking agents, since their non-determinism plus real-world consequences create the supervision challenge at the heart of the AI Coordination Gap.
How does multi-agent orchestration work?
Multi-agent orchestration coordinates several specialized agents — a planner, a researcher, an executor — under a controller that routes tasks and manages shared state. Frameworks like AutoGen, CrewAI, and LangGraph handle message passing, tool access, and handoffs. LangGraph models this as a graph where nodes are agents/tools and edges are conditional transitions. Governance lives at the edges: you insert risk-tiered interrupts so only high-blast-radius transitions escalate to humans. Done well, the orchestrator runs routine handoffs at machine pace — Google's 'agentic fleet' — while reserving scarce human attention for genuine exceptions, directly avoiding the approval-decay failure Amazon warns about.
What is MCP in AI?
MCP (Model Context Protocol) is an open standard, introduced by Anthropic, that lets AI models connect to tools and data sources through a consistent interface — like a universal adapter between agents and the systems they act on. For governance it matters because MCP servers expose only the tools you sanction, giving you a deterministic scoping layer: an agent physically cannot call a tool that isn't published to it. Combined with risk-tiered escalation, MCP forms the guardrail floor in the replacement architecture — never fatiguing, never hallucinating a permission. See the MCP specification for implementation details.
What companies are using AI agents?
Amazon runs agentic security at scale across AWS, per Brandwine's June 2026 interview. Google deploys an 'agentic fleet' for cyber-security work at machine pace, per COO Francis deSouza. Microsoft, under Nadella, builds 'loop learning' agentic systems, and IBM is structuring governance for agentic deployment. Beyond the giants, thousands of enterprises build on LangGraph, CrewAI, and n8n for support triage, ops automation, and security. The common thread in 2026 is the shift from blanket human approval toward oversight-based governance — because all of them hit the same volume ceiling on human supervision.
What is the difference between RAG and fine-tuning?
RAG (Retrieval-Augmented Generation) retrieves relevant documents from a vector database like Pinecone at query time and injects them into the prompt — ideal for changing knowledge and citations. Fine-tuning bakes patterns into model weights through training — ideal for fixed style, format, or domain behaviour. Nadella's 'private reinforcement learning environments... on real traces' is closer to fine-tuning's family: improving the model on your organization's actual data. In practice, governance-conscious teams use RAG for facts and reserve fine-tuning/RL for durable judgment, keeping deterministic guardrails on top of both so non-determinism never controls high-blast-radius actions.
How do I get started with LangGraph?
Install with pip install langgraph, then define a StateGraph with nodes (agents/tools) and edges (transitions). Start with a single agent, add a tool, then introduce the interrupt() mechanism for human escalation — but configure it for risk tiers, not blanket approval, per this article's worked example. The official LangGraph docs have quickstarts; our LangGraph guide covers production patterns. For ready-made scaffolds with guardrails built in, explore our AI agent library. Begin small, instrument approval latency as a fatigue signal, and only escalate HIGH-tier actions to keep human reviewers genuinely alert.
About the Author
Rushil Shah
AI Systems Builder & Founder, Twarx
Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows and multi-agent architectures in production. In one recent engagement, his team replaced a 3-person refund-approval queue at a mid-market e-commerce client with a risk-tiered LangGraph escalation layer, cutting reviewed volume from roughly 300 to 18 actions per day while eliminating two chargeback-fraud loops the human queue had been rubber-stamping. His audit of 12 enterprise agentic deployments found 9 operating with no documented escalation threshold — the data point behind this article's framing. He writes from real implementation experience, covering what works in production, what fails at scale, and where AI technology governance is heading next.
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