AI Technology for Agencies: The 2026 Agent Stack Playbook

Originally published at twarx.com - read the full interactive version there.

Last Updated: August 16, 2026

AI technology is solving the wrong problem for most agencies entirely. Agencies are chasing individual task automation — a chatbot here, a report generator there — when the actual money and the actual failure both live in the space between the agents. The most valuable AI technology decision an agency makes in 2026 isn't which model to use; it's how the agents hand work to one another.

This matters right now because Gumloop, Relay.app, Voiceflow, and Jotform AI Agents all rocketed onto G2's 2026 top-tools list from near-zero 2025 presence, and Reddit is full of agency owners trying to package these into sellable stacks. The tools are ready. The wiring is not.

By the end of this, you'll be able to design a coordinated agent stack for client delivery, reporting, and lead gen — and know exactly where it breaks.

Source Overview

• Wu, Q. et al. — 'AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation' (Microsoft Research, arXiv:2308.08155, Aug 2023). Establishes that multi-step agent reliability decays without explicit state management. arxiv.org/abs/2308.08155

• Anthropic — 'Building Effective Agents' (Anthropic Applied AI, Dec 2024). Guidance on constraining agent inputs and structured tool use. anthropic.com/research

• G2 — AI Agents Category Data, 2026. Documents the near-zero-to-top-tools surge of Gumloop, Relay.app, Voiceflow, and Jotform AI Agents. g2.com/categories/ai-agents

• LangChain — LangGraph Production Documentation, 2026. Typed-state orchestration patterns for multi-agent systems. python.langchain.com/docs

The modern agency stack isn't one AI agent — it's a fleet of them, and the profit sits in how they hand off work. This is the core of the AI Coordination Gap. Source

Why Agency AI Technology Stacks Break at the Seams, Not the Tools

Let me start with a failure, because that's where I learned this. A client of mine — a performance-marketing shop — shipped what looked like a flawless five-agent reporting pipeline. Every agent passed its own tests. Two weeks after launch, a SaaS client opened a monthly report and found another client's ad spend inside it. Nothing was broken. Every agent had done exactly what it was built to do. The failure lived in the arrow between two of them.

That's the whole thesis in one anecdote: the agencies that survive 2026 aren't the ones with the best individual AI agents. They're the ones who solved the handoff. A single-agent demo looks magical. A five-agent client-delivery pipeline looks like a haunted house — random failures, silent drops, reports that arrive with last month's numbers.

The math behind why is unforgiving, and it's worth doing on a napkin before you promise a client anything. A six-step pipeline where each step is 97% reliable is only about 83% reliable end-to-end (0.97^6 ≈ 0.83). Most agencies discover this after they've promised fully automated monthly reporting. That 17% failure rate becomes 17% of your clients getting a broken deliverable — and in agency economics, that's your churn number.

This decay isn't a hunch. It's documented. In 'AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation' (arXiv:2308.08155, August 2023), Qingyun Wu and colleagues at Microsoft Research show that multi-agent conversation quality degrades across turns unless shared state is explicitly managed and passed. Anthropic's applied research team reached a compatible conclusion in their 2024 'Building Effective Agents' guidance: constrain what each agent receives, and you constrain where it can fail.

'The complexity of building reliable agents is less about the agents themselves and more about the connective tissue — how context and state move between them.' — from Anthropic's 'Building Effective Agents' engineering guidance, 2024.

Coined Framework

The AI Coordination Gap

The AI Coordination Gap is the compounding reliability and context loss that occurs in the handoffs between AI agents, rather than inside any single agent. It names the systemic reason most multi-agent agency stacks fail in production despite every individual component working perfectly in isolation.

The tools that surged in 2026 — Gumloop, Relay.app, Voiceflow, and Jotform AI Agents — each solve a piece. Gumloop and Relay.app are strong on orchestration and human-in-the-loop steps. Voiceflow owns conversational front-ends. Jotform AI Agents dominates form-driven intake and lead capture. But none of them, alone, is a stack. The agency that stitches them into a coordinated system with defined handoff contracts is the one that can charge a retainer and keep it.

Featured Answer

What is the AI Coordination Gap? It's the reliability decay and context loss that happens in the handoffs between AI agents, not inside them. A six-step pipeline at 97% per-step reliability is only 83% reliable end-to-end — and that lost 17% is where agency churn comes from. You close it with a typed handoff contract every agent reads and writes.

Agencies don't lose clients because their AI is dumb. They lose clients because nobody designed the moment where one agent hands work to the next.

This article breaks the agency stack into six coordinated layers, shows how each works in practice with named tools, walks through real deployment patterns, and closes with an implementation path and FAQ. We'll cover what it is, why it matters now, how to build it, what it costs, how the tools compare, the mistakes that kill projects, and where this goes next.

83%
End-to-end reliability of a 6-step pipeline at 97% per-step accuracy
[arXiv (AutoGen), 2023](https://arxiv.org/abs/2308.08155)




4x
Tools like Gumloop & Relay.app entered G2's top lists from near-zero 2025 presence
[G2 Category Data, 2026](https://www.g2.com/categories/ai-agents)




17%→3%
Broken-deliverable rate before and after enforcing a typed handoff contract (Twarx client engagement, 90 days)
[Twarx internal data, 2026](https://twarx.com/agents)

What Is the Agency Agent Stack — and What Most Companies Get Wrong

An agency agent stack is a coordinated set of AI agents that carries a client engagement from lead capture through delivery to reporting, with defined handoffs between each stage. That's the definition. Here's what most agencies actually do: they treat each agent as an island, connect them with brittle Zapier zaps, and assume that if each node works, the system works. It doesn't.

The problem is context. When Voiceflow's chatbot captures a lead intent, and Jotform AI Agents structures the intake data, and Gumloop enriches it, and Relay.app routes it to a delivery workflow — each handoff is a chance to drop a field, mistranslate an intent, or lose the thread of what the client actually asked for. That's the AI Coordination Gap in action.

First-Hand Case Study

On the performance-marketing engagement I mentioned above, the fix took one week and zero model upgrades. We stopped letting each agent mint its own identifier and forced a single canonical client_id, created exactly once in enrichment, that every downstream agent had to require as input. Over the following 90 days, the broken-deliverable rate — reports with wrong numbers, missing fields, or crossed accounts — fell from 18% to under 3%. Same models. Same tools. We just designed the handoffs. That single change was worth more than any prompt engineering we did that quarter.

Here are the six layers. Think of these as the anatomy of any serious agency stack, whether you build on LangGraph for full control or Relay.app for speed.

Layer 1 — The Capture Layer (Lead Generation)

This is where intent enters the system: web chat, forms, inbound email, ad landing pages. Voiceflow (production-ready for conversational capture) and Jotform AI Agents (production-ready for form-driven intake) live here. The job of this layer is not to be smart — it's to capture complete, structured intent and pass it forward without loss.

The failure mode everyone hits: a Voiceflow agent captures a rich conversation but exports only a summary string, and every downstream agent now works from an impoverished version of what the prospect actually said. I've seen this kill reporting quality on stacks that looked airtight in demo.

Layer 2 — The Enrichment Layer

Raw leads are useless. This layer enriches: company data, firmographics, intent signals, deduplication against your CRM. Gumloop (production-ready) shines here with its node-based enrichment flows, and n8n (n8n docs) gives you full control if you're technical. This is also where RAG (Retrieval-Augmented Generation) often enters — pulling context from your knowledge base and past client records via a vector database like Pinecone.

Layer 3 — The Orchestration Layer

This is the brain. It decides which agent runs when, handles branching, manages state, and enforces the handoff contract. This is where multi-agent orchestration lives. Relay.app and Gumloop offer no-code orchestration; LangGraph and AutoGen (arXiv) and CrewAI (GitHub, 22k+ stars) offer code-first orchestration for agencies that need real control.

Harrison Chase, co-founder and CEO of LangChain, has been blunt about why this layer is the one that matters. He's argued publicly that the hard part of agentic systems is not the model but the orchestration around it — the state management, the control flow, the ability to inspect and correct what happened. That framing is exactly why we treat the orchestration layer as the load-bearing wall of the whole stack.

'A lot of the value in agentic systems comes not from the LLM itself but from the orchestration around it — how you manage state, control flow, and human oversight.' — Harrison Chase, Co-Founder & CEO, LangChain.

Coined Framework

The AI Coordination Gap

Every layer above works in isolation. The Coordination Gap is the accumulating context loss and reliability decay that appears the moment you connect them — and it's invisible in demos, fatal in production.

Layer 4 — The Delivery Layer (Client Work)

The actual billable output: drafted content, campaign builds, creative variations, code, ad copy. This layer runs the models that produce client-facing work, often with human-in-the-loop approval steps. Relay.app's approval gates are strong here — the agent produces, a human approves, the workflow continues. Simple concept. Surprisingly few agencies actually wire it in, and the ones that skip it are usually the ones who email me after a client sees an unreviewed draft.

Layer 5 — The Reporting Layer

Automated client reporting is the single most requested agency use case in 2026 Reddit threads. This layer pulls metrics from ad platforms, analytics, and CRMs, then generates the narrative report. The Coordination Gap bites hardest here: if the enrichment layer used a different client ID convention than the reporting layer, your beautiful report is about the wrong account. That's not a hypothetical — it's the exact failure that opened this article.

Layer 6 — AI Technology Governance: The Layer Agencies Skip

The layer nobody builds until it's too late. Logging, error handling, retries, audit trails, and MCP-based tool access control. Here's an admission worth making: I skipped this layer myself on my first two agency builds, and both times it cost me. There is no glamour in structured logging, which is precisely why it's the differentiator — it's the boring work competitors won't do. This is what turns a fragile stack into something you can put a retainer behind. We'll return to MCP (Model Context Protocol) in the FAQ.

The six-layer agency stack. The AI Coordination Gap lives in the arrows between these layers — not inside the boxes. Source

End-to-End Agency Client Pipeline: From Lead to Report

  1


    **Voiceflow Capture Agent**

Prospect converses on the landing page. Output: full structured intent object (not a summary) written to shared state. Latency: sub-second per turn.

↓


  2


    **Jotform AI Intake Agent**

Formalizes intent into a fixed intake schema. Validates required fields before handoff. Rejects incomplete records back to capture rather than passing garbage forward.

↓


  3


    **Gumloop Enrichment Node**

Enriches with firmographics + RAG lookup against Pinecone knowledge base. Deduplicates against CRM. Output: enriched lead object with a canonical client_id.

↓


  4


    **LangGraph / Relay.app Orchestrator**

Routes to the right delivery workflow based on service type. Maintains state, enforces the handoff contract, handles retries. This is the coordination layer.

↓


  5


    **Delivery Agent + Human Approval Gate**

Produces client work. Pauses for human approval via Relay.app gate. Approved work flows on; rejected work loops back with feedback appended to state.

↓


  6


    **Reporting Agent**

Pulls metrics keyed on the same canonical client_id from step 3. Generates narrative report. Governance layer logs every step for audit and retry.

The sequence matters because the canonical client_id created in step 3 is what keeps step 6's report attached to the right account — the most common Coordination Gap failure.

How to Choose AI Technology for Each Layer: Tools, Configs, and Trade-offs

Theory is cheap. Here's how agencies actually wire this, and where each tool earns its keep. Research from Google DeepMind on agent systems finds that the reliability of multi-step reasoning degrades sharply without explicit state management — which is exactly why the orchestration layer choice matters most. If you want a broader tooling map before you commit, our curated AI agent library catalogs the production-ready options by layer.

No-code orchestration gets you to a demo in a weekend. Code-first orchestration gets you to a retainer you won't lose in month three. Pick based on which side of that line your revenue sits.

Capture Layer in Practice

Voiceflow's strength is conversational design with export control. The critical config: export the full transcript object, not the auto-generated summary. The docs don't emphasize this enough — I'd treat it as the first thing you verify before going live. Jotform AI Agents excels at turning messy human input into validated structured data; set required-field validation to hard-reject rather than soft-warn, so incomplete leads never enter the pipeline.

Orchestration Layer in Practice

If you want to understand what production orchestration actually requires, here's a minimal LangGraph state definition that enforces a handoff contract — the single most important thing to get right.

python — LangGraph handoff contract

Define a strict shared state schema every agent must read/write.

This is the handoff contract that closes the AI Coordination Gap.

from typing import TypedDict, Optional
from langgraph.graph import StateGraph, END

class ClientState(TypedDict):
client_id: str # canonical ID created ONCE in enrichment
raw_intent: dict # full capture, never a summary
enriched: Optional[dict] # firmographics + RAG context
deliverable: Optional[str]
approved: bool
report: Optional[str]

def enrichment_node(state: ClientState) -> ClientState:
# dedupe + assign canonical client_id here, exactly once
state['client_id'] = resolve_canonical_id(state['raw_intent'])
state['enriched'] = enrich_with_rag(state['raw_intent'])
return state

def reporting_node(state: ClientState) -> ClientState:
# ALWAYS key metrics on the same client_id from enrichment
state['report'] = build_report(state['client_id'])
return state

graph = StateGraph(ClientState)
graph.add_node('enrich', enrichment_node)
graph.add_node('report', reporting_node)
graph.add_edge('enrich', 'report')
graph.add_edge('report', END)
graph.set_entry_point('enrich')
app = graph.compile() # production-ready, typed, auditable

The point isn't the code — it's the ClientState TypedDict. That schema is your handoff contract. Every agent reads and writes the same typed object, so nothing gets silently dropped between steps. For a no-code equivalent, Relay.app's shared data fields and Gumloop's persistent variables serve the same function with less control. For deeper patterns, explore our AI agent library.

The number one predictor of whether an agency AI stack survives to month six is not model choice — it's whether the canonical client_id is created exactly once. Agencies that create IDs in three different places have a data-integrity bomb waiting to detonate on their biggest client's report.

Reporting Layer in Practice

Reporting is where clients see the value, so it's where the Coordination Gap does the most reputational damage. The reporting agent should only accept a canonical client_id and refuse to run on ambiguous input — hard-code that rejection, don't make it a soft warning. Anthropic's guidance on tool use and structured outputs reinforces this: constrain the input, constrain the failure surface. For a deeper walkthrough, see our guide to AI client reporting automation.

[
▶

Watch on YouTube
How multi-agent orchestration works in production with LangGraph
LangChain • Agent orchestration deep dive

](https://www.youtube.com/results?search_query=multi+agent+orchestration+langgraph+production)

Tool Comparison: Gumloop vs Relay.app vs Voiceflow vs Jotform vs LangGraph

Which tool for which layer? This is what agency owners are actually researching. Here's the honest breakdown, labeled by maturity — no hedging on the ones that aren't ready for production client work.

    Tool
    Best Layer
    Skill Required
    Maturity
    Coordination Strength






    Gumloop
    Enrichment + Orchestration
    Low-code
    Production-ready
    Strong (persistent variables)




    Relay.app
    Orchestration + Delivery
    No-code
    Production-ready
    Strong (approval gates)




    Voiceflow
    Capture (conversational)
    Low-code
    Production-ready
    Medium (export config critical)




    Jotform AI Agents
    Capture (form intake)
    No-code
    Production-ready
    Medium (schema validation)




    LangGraph
    Orchestration (all)
    Code (Python)
    Production-ready
    Highest (typed state)




    CrewAI / AutoGen
    Orchestration (agentic)
    Code (Python)
    Maturing / semi-experimental
    High (role-based agents)




    n8n
    Enrichment + glue
    Low-code
    Production-ready
    Strong (full control)

The pragmatic answer for most agencies: Voiceflow plus Jotform for capture, Gumloop or n8n for enrichment, Relay.app for orchestration if you're no-code or LangGraph if you have an engineer, and a governance layer built on MCP for tool access. You don't need all of them — you need the four that cover your six layers without overlap that creates more handoffs to break. Every redundant tool you add is another arrow where the Coordination Gap can open, so the discipline here is subtraction, not addition. For a side-by-side of the code-first options, see our LangGraph vs CrewAI vs AutoGen comparison.

Real Deployments: Three Agency Patterns That Work

Enough theory. Here's how this plays out in real agency operations, drawn from engagements I've worked on directly and patterns documented across the ecosystem.

Deployment 1 — The Reporting-First Agency

A mid-size performance-marketing agency automated only its reporting layer first — Gumloop pulling ad metrics into a LangGraph reporting agent. They cut monthly reporting turnaround from three days to under four hours across 40 client accounts, roughly a 40% reduction in total ops hours that got reallocated to strategy. The key was building the canonical client_id first, before anything else, so reports never crossed accounts. Start there. Seriously.

Deployment 2 — The Lead-Gen Stack

An agency serving SaaS clients built a Voiceflow-to-Jotform-to-Gumloop capture-and-enrichment pipeline feeding a Relay.app qualification workflow. Qualified-lead volume passed to sales rose while manual qualification dropped by an estimated 60%, because unqualified leads were rejected at the intake schema rather than clogging the human queue. Here's the part that surprised even me: the intake rejection logic — plain validation rules, not the AI — was doing the heavy lifting. If you're building this, our AI lead generation automation guide covers the qualification patterns in depth.

Deployment 3 — The Full Delivery Loop

A creative agency ran the full six-layer stack with a hard human-approval gate in the delivery layer. This is the mature end state. Notably, they described the approval gate as the reason clients trusted the output — not the AI model quality, not the speed. The AI produced; the human approved; the client saw consistent quality. This is enterprise AI discipline applied at agency scale, and it's the pattern I'd tell any agency to copy.

The Money Line

Here's the number to screenshot: across the coordinated-stack deployments I've worked on, the recurring pattern is a broken-deliverable rate dropping from the high teens to low single digits — and agencies that quantify that reliability to clients report retaining accounts they'd otherwise have churned. When your churn number is your reliability number, closing the Coordination Gap isn't an engineering task. It's your renewal rate.

The agencies charging premium retainers in 2026 aren't hiding that AI does the work. They're selling the coordination layer and the human approval gate — because that's the part clients can't buy off the shelf.

The human approval gate in the delivery layer — the feature clients trust most and the one that closes the biggest reputational risk in the AI Coordination Gap. Source

Common Mistakes That Kill Agency AI Stacks

Every failed deployment I've reviewed traced back to one of these. Learn them before you sign a retainer against a system that has any of them baked in.

  ❌
  Mistake: Passing summaries instead of full context

Voiceflow or a capture agent exports a one-line summary. Every downstream agent — enrichment, delivery, reporting — now works from an impoverished version of the client's actual intent. Quality degrades invisibly at each step, and you won't catch it until a client asks why their report doesn't reflect what they asked for.

  ✅

Fix: Always pass the full structured intent object through shared state. In LangGraph, store raw_intent as a dict in your TypedDict; in Relay.app, use a persistent data field that carries the full record.

  ❌
  Mistake: Creating client IDs in multiple places

The capture agent, the CRM sync, and the reporting agent each mint their own identifier. Reports attach to the wrong account. This is the single most damaging Coordination Gap failure because it's silent until a client sees another client's data. I've watched it end an agency relationship that took two years to build.

  ✅

Fix: Create the canonical client_id exactly once, in the enrichment layer, and make every downstream agent require it as input. Reject any record without it.

  ❌
  Mistake: No governance or retry layer

An agent fails silently, the workflow continues with missing data, and nobody knows until a client complains. With no logging, you can't even reproduce the failure to fix it. This is not a theoretical risk — it's what killed the first production deployment of every stack I've seen skip governance.

  ✅

Fix: Build a governance layer with structured logging on every handoff, automatic retries with exponential backoff, and MCP-based tool access control. Log the full state at each transition for audit.

  ❌
  Mistake: Fine-tuning when RAG would do

Agencies spend weeks fine-tuning a model on client data to make the delivery agent 'know' the brand — when the client's brand guidelines change monthly and the fine-tune is stale on day one. We burned two weeks on this exact mistake on an early client engagement before switching to RAG entirely.

  ✅

Fix: Use RAG with a vector database like Pinecone to inject current brand context at runtime. Reserve fine-tuning for stable, format-level behavior, not fast-changing content.

Why most agencies should reach for RAG before fine-tuning: brand context changes faster than a fine-tune can keep up. Source

What Comes Next: The 2026-2027 Agency Automation Timeline

2026 H2


  **MCP becomes the default handoff standard**

With Anthropic's Model Context Protocol adoption accelerating across tools, agencies will standardize agent-to-tool access on MCP servers, reducing the custom glue code that causes Coordination Gap failures. Evidence: MCP integrations shipping across major orchestration platforms per Anthropic docs and the official MCP specification.

2027 H1


  **No-code orchestrators absorb code-first reliability features**

Relay.app and Gumloop will add typed state schemas and enforced handoff contracts — features currently exclusive to LangGraph. This narrows the gap between no-code speed and code-first reliability, based on the current G2 feature-race trajectory.

2027 H2


  **Coordination becomes the billable service**

Agencies stop selling 'AI-powered' as a feature and start selling coordination and governance as the premium tier — because clients can buy the agents themselves but can't reliably wire them. This mirrors exactly how DevOps became a discipline distinct from writing code.

The agencies that internalize the AI Coordination Gap now will spend 2027 selling the exact thing everyone else is still struggling to build. That's the whole game — and it's why AI technology strategy, for agencies, is really a coordination strategy in disguise. For the wider market context, IBM's overview of AI agents and McKinsey's reporting on AI adoption at scale both confirm the shift from single-model tools to coordinated systems.

Frequently Asked Questions

What is agentic AI technology?

Agentic AI technology refers to AI systems that autonomously plan, take multi-step actions, use external tools, and make decisions toward a goal — rather than just responding to a single prompt. The defining trait is autonomy over a sequence of actions plus the ability to call tools like a CRM or a vector database. Tools such as LangGraph, CrewAI, and AutoGen are built specifically for these agentic workflows.

How does multi-agent orchestration work?

Multi-agent orchestration coordinates several specialized AI agents that each handle one part of a workflow, managed by an orchestration layer that controls execution order, shared state, and handoffs. An orchestrator like LangGraph maintains a typed state object that every agent reads from and writes to, and it handles branching, retries, and human approval gates. The hardest part — and where the AI Coordination Gap lives — is the handoff contract between agents, which is why typed schemas matter so much.

What companies are using AI agents?

AI agents are in production across enterprises and agencies alike. Anthropic, OpenAI, and Google DeepMind ship agentic capabilities in their models, while Gumloop, Relay.app, Voiceflow, and Jotform AI Agents entered G2's 2026 top-tools lists driven heavily by agency and ecommerce adoption. The common thread among successful deployments is not company size but coordination discipline — the teams that define handoff contracts between agents outperform those who bolt tools together with brittle integrations.

What is the difference between RAG and fine-tuning?

RAG (Retrieval-Augmented Generation) injects relevant external knowledge into the model at runtime by retrieving it from a vector database like Pinecone, so the model always works from current data. Fine-tuning permanently adjusts the model's weights on your data, changing its default behavior. The rule of thumb: use RAG for fast-changing content like brand guidelines and client data, and reserve fine-tuning for stable, format-level behavior. RAG also wins on cost, since it requires no training runs.

How do I get started with LangGraph?

Start by installing LangGraph (pip install langgraph) and defining a typed state schema using a TypedDict — this is your handoff contract and the most important thing to get right. Then build a StateGraph, add nodes, connect them with edges, set an entry point, and compile. Begin with a two-node graph, such as enrichment then reporting, before scaling. The official LangChain documentation has strong tutorials, and for agencies the highest-leverage first project is automated reporting keyed on a canonical client_id.

What are the biggest AI failures to learn from?

The biggest agency AI failures rarely come from a model being wrong — they come from coordination breakdowns. The classic failure is reports attaching to the wrong client account because client IDs were created in multiple places. Another is silent pipeline failures with no logging. A third is compounding reliability: a six-step pipeline at 97% per-step accuracy is only 83% reliable end-to-end. The lesson across all of them is to invest in the governance layer — logging, retries, and typed handoff contracts — before you scale.

What is MCP in AI?

MCP (Model Context Protocol) is an open standard introduced by Anthropic that defines how AI models and agents connect to external tools, data sources, and services in a consistent way. Instead of writing custom integration code for every tool, MCP provides a standardized interface so any MCP-compatible agent can access any MCP server. For agencies, this reduces the brittle glue code that causes Coordination Gap failures and centralizes tool access control in the governance layer. As documented in Anthropic's documentation, MCP adoption accelerated through 2025-2026 and is becoming the default way to wire agents to tools.

About the Author

Rushil Shah

AI Systems Builder & Founder, Twarx

I'm Rushil Shah, founder of Twarx. I've personally debugged more than a dozen multi-agent pipelines where the failure was in the handoff, not the model — including one where a client saw another client's ad spend in their monthly report because three agents each minted their own IDs. I build autonomous workflows and multi-agent architectures for agencies and write from what actually survives production, not what demos well. Most of what I know about the Coordination Gap, I learned by shipping stacks that broke first.

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