---
title: "AI Agent for Sales: Real-World Applications Across the Sales Funnel"
url: "https://www.spaceotechnologies.com/blog/ai-agent-for-sales/"
date: "2026-09-23T11:53:02+00:00"
modified: "2026-09-23T11:53:05+00:00"
type: "Article"
resource: "https://www.spaceotechnologies.com/blog/ai-agent-for-sales/"
timestamp: "2026-09-23T11:53:05+00:00"
author:
  name: "Bhaval Patel"
categories:
  - "Artificial intelligence"
word_count: 3917
reading_time: "20 min read"
summary: "Key Takeaways
A real AI sales agent decides its next step on its own, using autonomy, reasoning, tool use, and memory. A feature just labeled "agent" without these four traits is usually rebranded ..."
description: "AI agent for sales explained with top 10 use cases, features, tools, benefits, and risks. Learn whether to buy a solution or build a custom sales AI agent."
keywords: "AI Agent for Sales, Artificial intelligence"
language: "en"
schema_type: "Article"
related_posts:
  - title: "AI Agent Architecture: Core Components, Design Patterns, and How to Choose the Right One"
    url: "https://www.spaceotechnologies.com/blog/ai-agent-architecture/"
  - title: "AI Agents for Real Estate: Use Cases, Benefits, and How to Use"
    url: "https://www.spaceotechnologies.com/blog/ai-agents-for-real-estate/"
  - title: "Generative AI in Software Development: Use Cases, Benefits, and How to Get Started"
    url: "https://www.spaceotechnologies.com/blog/generative-ai-in-software-development/"
---

# AI Agent for Sales: Real-World Applications Across the Sales Funnel

_Published: September 23, 2026_  
_Author: Bhaval Patel_  

![AI Agent for Sales Real-World Applications Across the Sales Funnel](https://www.spaceotechnologies.com/wp-content/uploads/2026/09/AI-Agent-for-Sales-Real-World-Applications-Across-the-Sales-Funnel-1024x541.webp)

Key Takeaways

- A real AI sales agent decides its next step on its own, using autonomy, reasoning, tool use, and memory. A feature just labeled “agent” without these four traits is usually rebranded automation.
- Pre-call intelligence, live objection guidance, and post-call CRM hygiene currently have the strongest evidence behind them. CRM hygiene alone can recover 15 to 20 minutes of a rep’s time per call.
- Buying suits a standard sales process living in one CRM, while building suits industry-specific rules or data spanning multiple systems. Cost and timeline should drive that decision, not default habit.

Sales reps spend less than a third of their time actually selling. Research, data entry, lead qualification, and follow-ups take up much of the rest. **An AI agent for sales is an autonomous system that handles these tasks by assessing context, choosing actions, and moving workflows forward with limited human input.** Unlike chatbots or rule-based automation, AI agents can decide what to do next based on the situation.

AI adoption is already widespread across sales teams. [Salesforce](https://www.salesforce.com/news/stories/sales-ai-statistics-2024/) found that **81% of sales teams were experimenting with or had implemented AI**, while [McKinsey](https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier) estimates generative AI could increase sales productivity by **3% to 5%**. The real challenge is knowing where these agents fit, which tools to evaluate, and where human oversight still matters. If you’re new to the technology, [learn how AI agents work](https://www.spaceotechnologies.com/blog/ai-agent-development-explained/) before exploring their sales applications.

This guide covers real-world AI agent use cases across the sales funnel, from lead research and qualification to outreach, follow-ups, and CRM management. You’ll also learn which tools to evaluate and when buying an existing solution makes more sense than building one.

For teams ready to build rather than simply evaluate AI agents,[ AI agent development services](https://www.spaceotechnologies.com/ai-agent-development-company/) offer a path from sales use case to production deployment. The work can include agent architecture, CRM and tool integrations, testing, security, and deployment around your specific sales workflows.

## What is an AI Sales Agent?

An AI sales agent is an autonomous AI system that can research prospects, qualify leads, personalize outreach, update CRM records, and schedule meetings with limited human input. It uses business context to decide what action to take next instead of following a fixed sequence.

Unlike [AI chatbots](https://www.spaceotechnologies.com/blog/what-is-ai-chatbot/) and basic automation, AI sales agents can **reason across multiple steps, access connected tools, and take action**. They can use CRM data, conversation history, email platforms, and calendars to move a sales workflow forward.

The key difference is simple: **chatbots respond, automation follows rules, and AI agents decide and act.**

## AI Sales Agents vs. Chatbots, Workflows, and Copilots

These four categories get confused constantly, and the simplest test is who decides the next step. A chatbot waits for a question. A workflow follows a fixed rule. A copilot suggests. An agent decides and acts within limits you set.

| **Capability** | **Chatbot** | **Workflow Automation** | **Sales Copilot** | **AI Sales Agent** |
|---|---|---|---|---|
| Starts work | Visitor asks a question | A preset rule fires | Rep asks for help | A goal or signal triggers it |
| Understands context | Only within the chat | None, unless coded in | Good, inside one app | Across CRM, email, and data |
| Decides next step | No | No, fixed logic only | Suggests, rep decides | Yes, within guardrails |
| Runs multi-step tasks | No | Yes, predefined steps only | No | Yes, and adapts as it goes |
| Writes to systems | Rarely | Yes, when a rule allows | Only through the rep | Yes, when permissions allow |

Understanding this distinction matters before evaluating any tool. A vendor calling its product an “agent” does not guarantee it clears this bar.

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## Types of AI Sales Agents

AI sales agents generally fall into **two core operating models: autonomous agents and assistive agents**. They can also be categorized by the sales tasks they perform, such as prospecting, qualification, outreach, CRM management, and meeting scheduling.

### 1. Autonomous AI sales agents

**Autonomous AI sales agents operate independently across multiple steps of the sales process with limited human intervention.** They can research prospects, identify buying signals, personalize outreach, qualify responses, update CRM records, and schedule meetings.

These agents are suited to repetitive, well-defined sales workflows where the agent can take action based on business goals, available data, and real-time results.

### 2. Assistive AI sales agents

**Assistive AI sales agents, or sales copilots, work alongside sales representatives rather than replacing their involvement.** They can summarize calls, update CRM records, surface account insights, suggest responses, and provide real-time recommendations during sales conversations.

These agents keep sales representatives involved while reducing manual work and providing relevant information throughout the sales process.

### Other functional types of AI sales agents

Depending on the sales workflow, AI agents can also specialize in specific revenue tasks:

- **Lead generation agents:** research prospects and identify potential accounts.
- **Lead qualification agents:** evaluate prospects, score intent, and route qualified leads.
- **Sales outreach agents:** personalize and manage prospect communication.
- **Follow-up agents:** monitor engagement and trigger relevant follow-ups.
- **CRM agents:** update records, log activities, and manage sales data.
- **Appointment-setting agents:** coordinate calendars and schedule meetings.
- **Sales research agents:** gather account, prospect, and market intelligence.
- **Sales support agents:** assist representatives with information, summaries, and next actions.

The right type depends on how much autonomy your sales workflow requires and where your team needs support. Many organizations combine autonomous and assistive agents, using automation for repetitive tasks while keeping sales representatives involved in important customer decisions.

## Must-Have Features in an AI Agent for Sales

An effective AI sales agent needs more than conversation capabilities. It should connect with sales systems, understand customer context, take actions, and operate within defined controls.

### 1. CRM integration

Connect with CRM platforms to access lead profiles, account history, deal stages, activities, and customer interactions. The agent should also update records automatically after completing sales tasks.

### 2. Lead qualification and scoring

Analyze prospect information, engagement, and intent to identify qualified leads. The agent should prioritize high-value prospects and route them to the appropriate sales representative.

### 3. Personalized outreach

Generate relevant emails and messages using prospect, account, and conversation data. The agent should adapt follow-ups based on responses and engagement rather than sending identical sequences.

### 4. Multi-step workflow automation

Handle complete workflows instead of isolated tasks. For example, an agent can research a prospect, qualify the lead, send outreach, update the CRM, and schedule a meeting.

### 5. Sales tool integrations

Connect with essential tools such as email, calendars, sales intelligence platforms, communication systems, and customer databases. These integrations allow the agent to retrieve information and take actions across the sales workflow.

### 6. Human oversight and controls

Provide approval points for sensitive or high-value actions. Sales teams should be able to review, approve, modify, or stop agent actions when human judgment is required.

### 7. Analytics and activity tracking

Track conversations, actions, outcomes, conversions, and agent performance. This gives sales teams visibility into what the agent is doing and where workflows need improvement.

Together, these features allow an AI sales agent to move beyond basic automation and manage meaningful sales workflows. The exact feature set should depend on the sales process, existing tools, and level of autonomy required.

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## Top 10 Use Cases for AI Agents for Sales

AI agents are changing how sales teams manage repetitive tasks, customer interactions, and revenue workflows. **AI Agents for sales** can operate across multiple stages, from prospecting and lead qualification to proposals, follow-ups, and opportunity management.

### 1. Intelligent lead qualification and scoring

Sales AI agents analyze website activity, email engagement, CRM records, and buying signals to qualify leads. They continuously update lead scores, identify high-intent prospects, and prioritize leads based on their likelihood of conversion.

### 2. Autonomous prospecting and AI SDRs

AI SDR agents research target accounts, gather firmographic information, identify relevant prospects, and initiate personalized outreach. They can manage multi-step prospecting workflows across email and chat while reducing the manual research required from sales representatives.

### 3. Personalized sales outreach

AI agents use prospect profiles, account data, previous interactions, and engagement signals to create personalized emails and messages. They can adjust messaging and follow-up actions based on prospect responses, engagement levels, and changes in buying intent.

### 4. Sales conversation intelligence

AI agents analyze sales calls and meetings to identify important topics, customer concerns, competitor mentions, and buying signals. They can summarize conversations, highlight key moments, and provide relevant insights that sales representatives can use during follow-ups.

### 5. Automated CRM management

AI agents capture information from emails, calls, meetings, and other sales activities. They can update contact records, opportunity fields, deal stages, and activity logs automatically, keeping CRM data accurate without requiring representatives to handle every update manually.

### 6. Discovery call and objection preparation

AI agent in sales helps analyze account information, previous conversations, and internal sales playbooks before upcoming meetings. They can prepare discovery questions, account research, talking points, and objection-handling suggestions based on the specific prospect and sales opportunity.

### 7. Proposal and quote generation

AI agents use customer information, pricing rules, product data, and historical sales information to generate customized proposals and quotes. They can also adapt pricing details, product recommendations, and document content to match specific customer requirements.

### 8. Automated follow-ups and lead nurturing

AI agent in sales monitors prospect engagement and determines when follow-up actions are appropriate. They can send personalized reminders, emails, and messages based on previous interactions, helping sales teams maintain consistent communication throughout longer buying cycles.

### 9. Meeting scheduling and appointment management

AI agents coordinate calendars, identify suitable meeting times, send scheduling options, and update appointments automatically. They can also manage rescheduling, confirmations, and reminders, reducing the administrative work associated with sales meetings.

### 10. Sales forecasting and opportunity insights

Sales AI agents analyze pipeline activity, deal history, engagement signals, and opportunity data to identify changes in deal momentum. They can surface potential risks, highlight important opportunities, and provide sales teams with timely insights for managing their pipeline.

Together, these AI Agent for sales use cases cover the core sales funnel, from finding and qualifying prospects to nurturing opportunities and advancing deals.

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## What Are the Benefits of an AI Agent for Sales Teams?

AI agents go beyond individual task automation by connecting sales data, workflows, and actions. The most important benefits for sales teams include:

### 1. Reduce manual sales work

An AI agent for sales handles repetitive tasks such as data entry, prospect research, lead qualification, scheduling, and follow-ups. This gives sales representatives more time for customer conversations and relationship building.

### 2. Respond to leads faster

Sales AI agents continuously monitor incoming leads and engagement signals. They can qualify prospects, trigger follow-ups, and route high-intent leads without waiting for a sales representative to review every interaction.

### 3. Improve sales productivity

By handling time-consuming administrative work, AI agents allow sales teams to manage more prospects with less manual effort. Representatives can focus their time on conversations and activities that directly support revenue generation.

### 4. Personalize customer interactions

AI agents combine customer data, previous conversations, account information, and engagement signals to personalize outreach. They can adjust messages and follow-ups based on each prospect’s interests, behavior, and buying stage.

### 5. Keep CRM data up to date

AI agents automatically capture information from sales calls, emails, meetings, and other interactions. This keeps customer and opportunity records current while reducing the time representatives spend manually updating CRM systems.

### 6. Improve lead prioritization

An AI agent in sales evaluates customer and engagement signals to identify prospects showing stronger buying intent. Sales teams can then focus their attention on high-priority opportunities instead of treating every lead equally.

Together, these benefits make AI agents valuable across the sales workflow. They reduce repetitive work while giving sales teams more time, context, and consistency to manage customer relationships.

## What Are the Risks and Limitations of AI Agents in Sales?

**An AI agent for sales introduces risks around data quality, accuracy, compliance, and adoption.** The most common limitations include:

- **Dirty CRM data:** Outdated or duplicate records can trigger inaccurate outreach.
- **Invented claims:** Poorly grounded agents may promise unavailable features, discounts, or integrations.
- **Deliverability damage:** Aggressive automated outreach can affect domain reputation and inbox placement.
- **Compliance gaps:** Agents still need proper consent, opt-out handling, and data protection controls.
- **Low rep adoption:** Sales teams may avoid agents that produce generic outputs or add unnecessary steps.
- **Broad implementation scope:** Automating too much at once makes it harder to identify and fix workflow issues.

Starting with a focused workflow, human oversight, and clear controls can reduce these risks. Following [best practices for developing AI agents](https://www.spaceotechnologies.com/blog/ai-agent-development-best-practices/) also helps teams establish proper data, security, testing, and monitoring practices before scaling.

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## Top AI Sales Agent Tools to Consider

The AI sales agent market includes platforms for prospecting, outreach, CRM management, deal intelligence, and sales execution. Here are several **AI sales agent tools** that sales teams can evaluate based on their workflows and automation needs.

### 1. Salesforce Agentforce

[Agentforce](https://www.salesforce.com/in/sales/ai-sales-agent/) provides AI agents that support sales activities across prospecting, lead engagement, pipeline management, and account growth. Its agents connect with Salesforce data and can execute sales actions within defined business rules and guardrails.

**Key capabilities:**

- Prioritizes prospects and supports continuous pipeline generation.
- Personalizes lead engagement using sales and customer data.
- Automates sales tasks, including record updates and follow-ups.
- Supports quoting, lead nurturing, and sales representative coaching.
- Connects agents with Salesforce data, workflows, and business logic.

### 2. HubSpot Prospecting Agent

[HubSpot’s Prospecting Agent](https://www.hubspot.com/products/sales/ai-prospecting-agent) focuses on account research, contact discovery, buying signals, and personalized outreach. It monitors accounts continuously and helps sales representatives identify who to contact and when.

**Key capabilities:**

- Researches target accounts and identifies relevant decision-makers.
- Monitors buying signals across accounts and prospect activity.
- Sources and enriches contacts from connected data providers.
- Drafts personalized outreach based on prospect and account context.
- Supports continuous prospecting without constant manual research.

### 3. 11x

[11x](https://www.11x.ai/) uses autonomous digital workers for outbound and inbound sales functions. Alice handles outbound prospecting, while Julian responds to inbound leads, qualifies them, and routes meetings to sales representatives.

**Key capabilities:**

- Researches accounts and identifies prospects for outbound campaigns.
- Creates personalized outreach across multiple communication channels.
- Handles prospect replies and continues sales conversations.
- Qualifies inbound leads against defined ideal customer profiles.
- Books qualified meetings and routes them to sales representatives.

### 4. Artisan Ava

[Ava by Artisan](https://www.artisan.co/ai-sales-agent) is an autonomous AI BDR designed for end-to-end outbound sales. It handles prospect research, personalized outreach, replies, and meeting booking with configurable approval points.

**Key capabilities:**

- Identifies and researches prospects before initiating outreach.
- Creates personalized messages based on individual prospect research.
- Runs outbound email sequences across targeted prospect lists.
- Handles prospect replies, questions, and common objections.
- Books qualified meetings directly onto sales representatives’ calendars.

### 5. Outreach AI Agents

[Outreach AI Agents](https://www.outreach.ai/ai-agents) support different stages of the sales process, including research, personalization, revenue generation, deal management, and meeting preparation. Its agents can automate recurring sales workflows while allowing teams to configure specific processes.

**Key capabilities:**

- Researches prospects and accounts using defined sales criteria.
- Creates personalized email, call, and LinkedIn messaging.
- Identifies prospects matching predefined ideal customer profiles.
- Supports opportunity management and deal progression.
- Prepares representatives for upcoming customer meetings.

### 6. Gong Agents

[Gong Agents](https://www.gong.io/platform/ai-agents-for-revenue-teams) support revenue teams with specialized workflows across deal management, forecasting, coaching, and customer engagement. Agents use conversation, CRM, email, and revenue data to provide context-aware insights and automate recurring tasks.

**Key capabilities:**

- Analyzes customer conversations and identifies important sales signals.
- Generates structured account, contact, and opportunity briefs.
- Identifies deal risks, trends, and revenue opportunities.
- Supports forecasting, coaching, and sales performance workflows.
- Automates recurring revenue tasks using business-specific context.

These tools differ in their primary focus and level of autonomy. Some emphasize autonomous prospecting, while others connect AI agents with CRM, revenue intelligence, deal management, and broader sales workflows.

If you plan to build a custom AI sales agent rather than adopt an existing platform, choosing the right framework becomes important. Explore our [guide to AI agent frameworks](https://www.spaceotechnologies.com/blog/ai-agent-frameworks/) to compare frameworks used for building and orchestrating AI agents.

## Should You Buy an AI Sales Agent or Build a Custom One?

The choice depends on your sales process, required integrations, level of customization, and budget. **Buying an existing AI sales agent works well for standardized workflows, while custom development suits teams with complex processes or unique requirements.**

### Buy an AI sales agent when

An existing platform is usually practical when you need to automate common sales activities quickly. These tools typically provide ready-made integrations, workflows, and features without requiring extensive development.

- You need prospecting, outreach, or qualification automation quickly.
- Your sales workflows closely match the platform’s available features.
- You want predictable subscription costs and faster deployment.
- Your existing CRM and sales tools have supported integrations.
- You do not need extensive customization or proprietary workflows.

### Build a custom AI sales agent when

Custom development makes more sense when your sales process involves specialized workflows, proprietary data, or multiple business systems. It also gives you greater control over the agent’s behavior, integrations, security, and scalability.

- Your sales process includes complex or highly customized workflows.
- The agent needs integrations with proprietary or legacy systems.
- You require custom business rules, logic, or approval workflows.
- Your team needs greater control over data and agent behavior.
- You plan to scale the agent across multiple sales processes.

Custom development also requires a larger upfront investment and ongoing maintenance. Before choosing this approach, review the factors that influence the [cost of AI agent development](https://www.spaceotechnologies.com/blog/ai-agent-development-cost/), including complexity, integrations, technology stack, and development requirements.

If you decide to build rather than buy, working with an experienced development partner can reduce implementation challenges. You can also compare [top AI agent development companies](https://www.spaceotechnologies.com/blog/ai-agent-development-companies/) based on their expertise, technical capabilities, experience, and ability to support your specific sales requirements.

The right approach depends on the complexity and long-term requirements of your sales workflow. A ready-made platform can address standardized needs, while custom development provides greater flexibility when existing tools cannot accommodate your processes.

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## A Practical Implementation Plan for an AI Agent for Sales

Implementing AI agents across the entire sales process at once can create unnecessary complexity. Before starting, review the [process of AI agent development](https://www.spaceotechnologies.com/blog/ai-agent-development-process/) to understand the broader steps involved in planning, building, testing, and deploying an agent.

A phased rollout then lets sales teams validate each workflow, measure results, and expand automation based on what works.

### 1. Choose one high-value sales workflow

Start with a repetitive workflow that has clear inputs, outputs, and measurable results. Lead qualification, prospect research, or follow-ups are practical starting points because their outcomes are easier to track.

### 2. Define the agent’s role and boundaries

Specify what the agent should handle and where human involvement remains necessary. Define its actions, approval points, escalation rules, and tasks that require sales representative intervention.

### 3. Connect the required sales data

Integrate the agent with relevant CRM records, customer data, email platforms, calendars, and other sales systems. Clean outdated or duplicate data before giving the agent access to production workflows.

### 4. Test with a limited group

Deploy the agent to a small sales team or selected workflow before expanding its use. Monitor accuracy, response quality, task completion, and representative feedback during this stage.

### 5. Measure business and agent performance

Track metrics that reflect the workflow’s actual goals. Depending on the use case, monitor response time, qualified leads, meeting bookings, conversion rates, task completion, and human intervention.

### 6. Refine and expand gradually

Use performance data and sales representative feedback to improve prompts, workflows, integrations, and approval rules. Once the initial workflow performs consistently, introduce the agent to additional sales processes.

A phased rollout keeps AI adoption measurable and controlled. Starting with one focused workflow gives sales teams time to establish reliable processes before expanding agent autonomy across the wider sales funnel.

## Building Custom Sales AI Agents With Space-O Technologies

Space-O Technologies builds custom AI agents around specific sales workflows, business rules, and technology environments. Our team designs agents that connect with CRM systems, sales platforms, customer data, and other business tools.

We focus on practical sales automation rather than adding AI to workflows that do not need it. From identifying the right use case to testing, integration, deployment, and ongoing optimization, we build the agent around your team’s operational requirements.

**What we deliver:**

- **Custom AI sales agent development** tailored to specific sales workflows.
- **CRM and sales tool integration** for connected data and automated actions.
- **Lead qualification and prospecting workflows** based on defined business criteria.
- **Personalized outreach automation** across relevant sales communication channels.
- **Human-in-the-loop controls** for approvals, escalations, and sensitive actions.
- **Testing and monitoring** to evaluate agent accuracy, reliability, and performance.

If you’re evaluating a custom solution, [hire dedicated AI agent developers](https://www.spaceotechnologies.com/hire/ai-agent-developers/) to build and integrate sales agents around your existing technology stack.

## Frequently Asked Questions

### What is an AI agent for sales?

An AI agent for sales is software that takes a goal, reads context from the CRM, and acts on it. Such an agent can score leads, send follow-ups, or book meetings with limited human supervision at each step. Most teams deploy a small set of agents, each owning one narrow part of the funnel.

### How is an AI sales agent different from a chatbot?

A chatbot waits for a question and responds within a single conversation. An AI sales agent works toward a goal across multiple steps. Reasoning over context and taking real action in systems like the CRM sets it apart from a chatbot entirely.

### Will AI sales agents replace human sales reps?

No, AI sales agents are built to remove repetitive research and admin work, not replace relationship-driven selling. Complex negotiations and multi-stakeholder deals still need a rep’s judgment and trust-building skills. Transactional, lower-value sales tend to shift toward automation first.

### What are the most proven AI sales agent use cases right now?

Pre-call intelligence, live objection guidance, and post-call CRM hygiene currently have the strongest evidence behind them. CRM hygiene alone can recover 15 to 20 minutes of a rep’s time per call. Newer use cases like autonomous full-cycle outreach remain more experimental for most B2B sales motions.

### How much do AI sales agents cost?

Costs vary widely based on pricing model, from per-seat licenses to per-lead or per-meeting pricing for specialist tools. Custom-built agents carry an upfront development cost but avoid ongoing per-lead fees entirely. Modeling total cost against real lead volume gives a clearer picture than any single price point alone.

### Should a small sales team use AI agents too?

Yes, smaller teams often see a proportionally larger benefit, since an agent lets a small team cover more accounts. Capacity gained this way often matters more for a small team than for one already fully staffed. Off-the-shelf CRM-native agents typically suit a small team’s budget and timeline best.

### What is the biggest risk when deploying an AI sales agent?

Dirty CRM data is the most common root cause of AI sales agent failures. An agent inherits whatever mess already exists in the data, then acts on it automatically, at far greater speed. Auditing data quality before connecting any agent prevents most of this risk entirely.

### How long does it take to see results from an AI sales agent?

Most teams see measurable results within 90 days when they start with one narrow use case. A staged rollout with a clear baseline makes that improvement provable rather than anecdotal. Trying to automate several workflows at once usually pushes that timeline out considerably.


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