Agentic-First Agency: Complete Guide to AI-Powered Marketing
A single person can now operate a full-service digital marketing agency with AI agents, delivering work that previously required teams of 10-20 people. This is the complete guide to building and running an agentic-first agency from service design to pricing to client delivery.
Workload Reduction Per Client
Profit Margins vs 15-25% Traditional
Clients Managed by One Person
Monthly Revenue Per Client
Key Takeaways
The End of the Traditional Agency Model
The traditional digital marketing agency model is built on a straightforward premise: hire specialists for each channel, manage overhead, and scale revenue by adding more clients and more people. An SEO specialist, a PPC manager, a content writer, a social media coordinator, a web developer, a designer, an account manager, and a project manager. Ten people to serve ten clients. Twenty to serve twenty.
This model worked for decades because marketing execution required human judgment at every step. Writing ad copy, analyzing campaign data, optimizing landing pages, building websites, managing social communities — each task demanded a skilled person. Revenue scaled linearly with headcount, and margins stayed thin at 15-25% after salaries, office space, software subscriptions, and management overhead.
That premise no longer holds. AI models released through 2025 and into 2026 — Claude Opus 4.6, GPT-5 series, Gemini 3 — can now handle complex, multi-step marketing tasks autonomously. Not as assistants that suggest edits. As agents that execute entire workflows: researching keywords, writing optimized content, generating ad variations, analyzing campaign performance, and building client dashboards.
The data supports the shift. Agency headcounts fell 8% industry-wide in 2025. WPP cut 7,000 employees. Dentsu eliminated 3,400 jobs. IPG and Omnicom reduced headcount by another 6,200 combined. These are not layoffs from a downturn — they are structural reorganizations around AI capabilities. Meanwhile, early AI adopters are reporting 40% higher revenue and 75% workload reduction per client.
Sam Altman predicted the first one-person billion-dollar company would emerge by 2026-2028. While that scale remains aspirational, the underlying dynamic is already visible: companies like Mercor achieve $4.5M revenue per employee (versus Microsoft at $1.8M), and solo founders using AI agents are building seven-figure operations with near-zero headcount. The agentic-first agency model is the practical version of this trend for professional services.
What “Agentic-First” Actually Means
“Agentic-first” is a specific operating model, not a marketing buzzword. It means every service offering is designed around AI agent workflows from the start — not bolted on after the fact. The distinction matters because it changes everything about how services are scoped, priced, delivered, and scaled.
Consider the difference between three models of AI adoption in agencies:
A specialist performs each task manually. The agency hires an SEO specialist who conducts audits, writes recommendations, and implements changes. Revenue scales linearly with team size.
The same specialist uses AI tools to speed up their work. They prompt ChatGPT for content drafts, use AI for keyword research, or generate ad variations faster. The human still drives every task.
AI agents execute entire workflows autonomously. The owner designs the workflow, triggers the agent, reviews the output, and delivers to the client. The AI does the work; the human ensures quality.
The Four Service Types
In an agentic-first agency, every service falls into one of four categories based on how much AI agent involvement is possible:
Setup Services
One-time configuration tasks. DNS setup, Google Workspace configuration, analytics pixel installation. Agents can generate configuration files and instructions; the owner executes and verifies.
Agentic Services
Ongoing work driven primarily by AI agents. Blog content creation, ad campaign management, social media scheduling, database schema design, landing page development. The agent executes; the owner reviews and approves.
Research Services
AI-assisted analysis and investigation. Platform selection research, ecosystem design, competitive analysis. Agents gather and synthesize data; the owner interprets findings and makes recommendations.
Consulting Services
Human-led strategic work. Vendor advisory, stakeholder presentations, crisis communication. Agents prepare materials and analysis; the owner leads the engagement directly.
The orchestrator role is the defining characteristic of the agentic-first model. You are the conductor, not the musician. Your job is to design agent workflows, monitor their execution, verify output quality, manage client relationships, and make strategic decisions that AI agents are not yet equipped to handle. This is a fundamentally different skill set from traditional agency management — closer to systems engineering than project management.
Why This Is Only Possible Now (2026)
The concept of AI-assisted marketing is not new. What changed in late 2025 and early 2026 is a convergence of capabilities that crossed a critical threshold. LLM agent capabilities reached what Anthropic's 2026 Agentic Coding Trends Report calls “a threshold of coherence” around December 2025 — the point where AI agents can reliably complete multi-step tasks without losing context or making compounding errors.
200K+ token context windows mean agents can hold an entire client brief, brand guidelines, competitive analysis, and previous deliverables in memory while working. No more fragmented, context-free generations.
AI models can now reliably call APIs, query databases, browse the web, execute code, and interact with third-party platforms. This transforms them from text generators into actual operational agents.
Models maintain coherent plans across dozens of sequential steps. An SEO audit agent can crawl a site, identify issues, prioritize by impact, generate fix recommendations, and create implementation tickets — all in one workflow.
AI coding agents can build complete client infrastructure — dashboards, integrations, landing pages, full-stack applications. Claude Code completed a task on a 12.5-million-line codebase with 99.9% accuracy in 7 hours of autonomous work.
The coding agent breakthrough deserves special attention. Traditional agencies outsource development or hire developers. An agentic-first agency uses AI coding tools — Claude Code, Cursor, GitHub Copilot — to build client websites, dashboards, integrations, and automation systems. This capability alone eliminates what was previously the most expensive line item in agency operations.
The AI agents market is projected to exceed $10.9 billion in 2026, up from $7.6 billion in 2025 — a 45%+ compound annual growth rate. By end of 2026, 40% of enterprise applications are expected to integrate task-specific AI agents, up from less than 5% in 2025. This is not a future possibility. It is the current reality for agencies willing to adopt the model.
The Agentic Service Stack: Mapping Traditional Services to Agent Workflows
Every traditional digital marketing service can be decomposed into structured agent workflows. The following breakdown covers all nine service areas, showing how each translates from human-dependent tasks into agent-driven processes with human verification checkpoints.
SEO Optimization Agent Workflows
Technical Audit Agent
Crawls client sites, identifies broken links, missing meta tags, slow pages, mobile issues, and schema errors. Generates prioritized fix recommendations with implementation code snippets.
Content Creation Agent
Performs keyword research, builds topic clusters, generates full blog posts optimized for search intent, and creates internal linking strategies. AI-enhanced content services can increase output by 300-500% while maintaining quality.
On-Page Optimization Agent
Audits and generates optimized meta titles, descriptions, heading structures, image alt text, and structured data markup. Outputs ready to implement.
Link Building Research Agent
Identifies link building opportunities through competitor backlink analysis, broken link discovery, and resource page prospecting. Drafts personalized outreach emails.
Traditional cost: €799-€1,999/month for a dedicated SEO specialist. Agentic approach: Same deliverables, fraction of the labor cost, because the SEO agent workflows execute in minutes rather than hours.
PPC Advertising Agent Workflows
Campaign Structure Agent
Researches audience segments, groups keywords by intent, generates ad copy variations, and structures campaigns for Google Ads, Meta Ads, and Microsoft Ads. Agencies using AI for PPC report up to 280% ROAS in documented case studies.
Bid Optimization Agent
Monitors campaign performance, recommends bid adjustments, reallocates budgets across channels based on performance data, and flags underperforming ad groups.
Creative Generation Agent
Produces ad copy variations, generates A/B test hypotheses, creates responsive search ad combinations, and drafts display ad concepts with headline and description permutations.
Our PPC advertising services combine these agent workflows with human strategic oversight to optimize campaigns across platforms.
Content, Social Media & Analytics Agent Workflows
- Content strategy & gap analysis
- Blog posts, case studies, whitepapers
- Cross-platform distribution
- Performance tracking & optimization
- Content calendar & scheduling
- Community monitoring & response
- Paid social optimization
- Engagement & competitor analysis
- GA4, GTM, pixel setup
- Custom dashboard generation
- Cross-channel attribution modeling
- Trend forecasting & opportunity identification
Web Development, eCommerce, CRM & AI Transformation
Architecture agents scaffold Next.js projects and design component systems. Implementation agents build full-stack features using Claude Code. QA agents run automated testing, accessibility audits, and performance optimization.
Store setup agents configure platforms and product catalogs. Conversion agents run A/B tests and optimize checkout flows. Integration agents connect inventory, fulfillment, and payment systems.
CRM configuration agents set up pipelines, field mappings, and automation rules. Lead nurturing agents design email sequences and scoring rules. Integration agents build API connections and data sync workflows.
Assessment agents evaluate client tech stacks and identify AI opportunities. Implementation agents deploy chatbots, automate workflows, and integrate AI features. Documentation agents create SOPs and training materials.
The Unit-Based Pricing Model
Traditional agency pricing models — hourly rates, fixed retainers, per-deliverable fees — create perverse incentives in an agentic-first world. Hourly billing punishes efficiency: if an AI agent completes a task in 10 minutes that used to take 4 hours, billing hourly means you earn less for the same outcome. Per-deliverable pricing is better but still couples revenue to output volume rather than value delivered.
Unit-based pricing solves this. Clients purchase units of work capacity rather than hours or specific deliverables. Each service has a defined unit cost based on the complexity and value it delivers, not the time it takes. This means:
- Efficiency gains benefit both sides — you deliver faster, clients get results sooner
- Pricing reflects value, not labor time
- Clients get transparent visibility into how their budget is allocated
- Revenue is predictable and recurring
Three-Tier Package Structure
- 20 units of work capacity
- 2 strategy sync calls/month
- Architecture & planning included
- Ideal for early-stage projects
- 30 units of work capacity
- 3 strategy sync calls/month
- Accelerated development pace
- Priority support included
- 40 units of work capacity
- 4 strategy sync calls/month
- Full-speed execution across workstreams
- Multiple concurrent projects
Additional units are available at €500 per 5 units as one-time add-ons. The unit economics work out to €100 per unit across all tiers, creating consistency and predictability for both the agency and the client. With 23% VAT (standard for Slovakia-based EU agencies), a Growth package invoices at €3,690 inclusive.
The Two-Tier Confirmation System
A critical feature of unit-based pricing is the two-tier service confirmation: selection and locking. Clients first browse available services and toggle them on and off freely — no units are deducted at this stage. When they are ready to commit, they lock their selections, which deducts units from their available balance and initiates the work. This gives clients full control over their allocation while preventing accidental overcommitment.
This pricing model aligns with the broader industry trend. Research from Digital Agency Network shows 78% of agencies now use retainer-based pricing (up from 64% in 2023), and outcome-based contracts result in 34% higher client renewals. Unit-based pricing captures the best of both: predictable recurring revenue with outcome-oriented accountability.
Client Experience: The Agentic Dashboard
The traditional agency client experience is opaque: weekly status emails, monthly reports, quarterly strategy reviews. The client trusts the agency is doing good work but has limited real-time visibility into what is happening, how their budget is being spent, or what deliverables are in progress.
An agentic-first agency replaces this with a transparent, self-service client dashboard. The client portal becomes the primary interface for the entire relationship — replacing most emails, calls, and status meetings with real-time visibility.
Core Dashboard Features
Project Overview
Team contacts, engagement model, project goals, and architecture documentation in one place.
Service Selection
Browse service categories, toggle services on/off, review unit costs, and lock selections when ready to commit.
Billing & Unit Tracking
Real-time unit balance (available, used, committed), pro forma invoices, and integrated Stripe payment.
Service Status Tracking
Every service moves through a visible workflow: selected → locked → in_progress → completed. No guesswork.
Timeline & Milestones
Project timeline with milestone tracking, so clients see progress without asking for updates.
Asset Management
Centralized file storage for brand assets, deliverables, and project documentation.
The trust-through-transparency advantage is real. When clients can see exactly what is happening with their project at any time, the volume of “just checking in” emails drops dramatically. The dashboard becomes the single source of truth, replacing the account manager role entirely. Client retention improves because satisfaction comes from visibility, not from a person reassuring them on a weekly call.
The Automated Intake-to-Delivery Pipeline
The full client lifecycle — from first website visit to ongoing service delivery — can be structured as an automated pipeline with minimal manual intervention. Each step feeds the next, and the owner intervenes only at strategic decision points.
Service Discovery
Prospective clients browse service pages that clearly communicate the agentic-first approach, pricing model, and expected outcomes.
Intake Form or AI Assistant
A multi-step intake form (or AI intake assistant) captures project type, services needed, budget range, and timeline.
AI Pre-Qualification
An AI agent analyzes the submission, pre-qualifies the lead, and generates a preliminary scope document with recommended services and estimated unit requirements.
Owner Review & Approval
The human verification checkpoint. You review the AI-generated scope, adjust as needed, and approve the proposal before it reaches the client.
Dashboard Access & Service Selection
Client receives dashboard access with proposed services. They browse, select, and lock their preferred services. A pro forma invoice generates automatically.
Payment & Agent Workflows Kick Off
Client pays via Stripe. Payment confirms, units activate, and AI agent workflows begin executing for each locked service. The owner monitors progress, reviews outputs, and delivers to the client.
The entire pipeline from intake to active service delivery can operate in 24-48 hours — compared to the traditional agency cycle of 1-2 weeks for proposals, negotiations, contracts, and onboarding. The speed advantage compounds over time: faster onboarding means faster revenue recognition and higher client satisfaction from immediate progress.
The AI Agent Tech Stack
An agentic-first agency relies on a curated stack of AI tools, each selected for a specific role in the service delivery pipeline. The goal is not to use every tool available but to build a coherent system where each component excels at its designated task.
| Category | Tools | Use Case |
|---|---|---|
| Coding & Development | Claude Code (Opus 4.6), Cursor, GitHub Copilot | Full-stack development, client dashboards, integrations |
| Content & Copy | Claude Sonnet 4.5, GPT-5 series, Gemini 3 | Blog posts, ad copy, social content, email sequences |
| Image & Design | Midjourney, DALL-E, Figma AI | Visual content, brand assets, ad creatives |
| Workflow Automation | n8n, Make.com, Zapier | Agent orchestration, data pipelines, cross-platform automation |
| CRM & Email | HubSpot/Zoho, Resend, React Email | Contact management, transactional email, lead nurturing |
| Analytics | GA4, GTM, Segment | Performance tracking, attribution, custom reporting |
| Web Infrastructure | Next.js, Vercel, Supabase, Cloudflare | Hosting, database, auth, CDN, storage |
| Payments | Stripe | Payment links, subscriptions, invoicing, webhooks |
Cost Comparison
A complete agentic-first agency stack costs between €3,000-€12,000 annually. This covers AI model subscriptions, hosting, automation tools, and infrastructure. Compare this to a traditional 10-person agency where salaries alone exceed €300,000-€500,000 annually, plus office rent, equipment, management software, and benefits. The cost structure difference is the fundamental economic advantage that makes the model viable.
The choice between automation platforms matters. n8n excels at complex agent orchestrations with its AI-native architecture and LangChain integration. Make.com offers a visual scenario builder ideal for multi-step logic at scale. Zapier provides the fastest implementation with 8,000+ pre-built integrations but less flexibility for custom agent workflows. Most agentic agencies use a combination: Zapier for simple connections, n8n or Make.com for complex orchestrations.
Building the Infrastructure: What You Actually Need
The minimum viable agentic agency requires six infrastructure components. Each one can be built incrementally, but all six are needed for a professional operation that can onboard and serve clients reliably.
1. Professional Website
Your storefront and service catalog. Communicates your agentic-first approach, showcases all service offerings, and drives intake form submissions. Built with a modern stack (Next.js recommended) that demonstrates your own technical capability.
2. Client Dashboard
Project management, billing, service selection, and status tracking. This replaces account managers, status emails, and weekly meetings. Building it yourself proves your development capability to clients.
3. Intake & Onboarding System
Multi-step form or AI intake assistant that captures project requirements, qualifies leads, and feeds data into your pipeline. The faster you onboard, the faster you generate revenue.
4. Billing System
Unit-based packages, pro forma invoicing, and Stripe-integrated payment processing. Handles subscription management, add-on purchases, and VAT calculations automatically.
5. AI Agent Workflows
The actual service delivery engine. Structured workflows for each service offering that agents can execute with defined inputs, outputs, and verification steps. This is the core of your competitive advantage.
6. Communication System
Async-first communication with scheduled sync calls. The dashboard handles most communication needs; sync calls are reserved for strategy discussions and complex decisions.
The Economics: One Person vs Traditional Agency
The financial case for the agentic-first model is compelling at every scale. The structural difference is straightforward: a traditional agency scales revenue by adding people, while an agentic-first agency scales revenue by adding clients without adding headcount.
| Cost Category | Traditional 10-Person Agency | Agentic-First (1 Person) |
|---|---|---|
| Salaries & Benefits | €300,000-€500,000/year | €0 |
| Office & Equipment | €30,000-€60,000/year | €0 (remote) |
| AI Tool Subscriptions | €5,000-€10,000/year | €3,000-€12,000/year |
| Software & Hosting | €15,000-€25,000/year | €2,000-€5,000/year |
| Management Overhead | €50,000-€80,000/year | €0 |
| Total Annual Cost | €400,000-€675,000 | €5,000-€17,000 |
| Typical Profit Margin | 15-25% | 60-80% |
Revenue Potential
Managing 5-8 active clients at €2,000-€4,000 per month each generates €10,000-€32,000 in monthly revenue. With annual costs of €5,000-€17,000 (primarily AI subscriptions and hosting), the take-home income after expenses ranges from €100,000 to €350,000+ annually. This compares favorably to the €50,000-€80,000 salary a marketing director would earn at a traditional agency, with the added benefit of business ownership equity.
The scaling paradox is the most counterintuitive aspect of the model: you scale revenue without scaling your team. Each additional client adds revenue with minimal incremental cost (a few more AI API calls and some additional oversight time). Traditional agencies face the opposite: each new client eventually requires additional hires, which increase fixed costs and compress margins.
Quality Assurance: The Human Verification Layer
The agentic-first model does not mean unverified AI output goes directly to clients. The human verification layer is the differentiator between a professional agency and a prompt-and-ship operation. The 80/20 principle applies: AI agents handle 80% of the execution, and you invest your time in the 20% that requires human judgment.
Where Human Checkpoints Are Non-Negotiable
Every campaign strategy, content calendar, and project plan gets human review before execution begins. AI generates the plan; you validate it against business objectives, client preferences, and market context that agents may miss.
All client-facing content is reviewed before publication. Check for brand voice consistency, factual accuracy, appropriate tone, and strategic alignment. AI content is good but not always brand-perfect without human polish.
All code generated by AI agents is reviewed before deployment. Automated testing, linting, and type checking catch most issues, but architectural decisions and security implications require human evaluation.
Sensitive conversations, contract negotiations, escalations, and strategic discussions remain human-led. The dashboard handles routine communication; you handle the conversations that build relationships.
Building review workflows into your service delivery is straightforward. Each agent workflow produces a deliverable in a staging state. You review, approve or request revisions, and then the deliverable moves to production. This is functionally identical to how traditional agencies operate with junior staff producing work that senior staff review — except the “junior staff” are AI agents that work 24/7 and produce first drafts in minutes instead of hours. Client transparency about this process builds trust rather than eroding it.
For New Agency Founders: Starting Agentic-First
Starting agentic-first has a distinct advantage over transitioning: you have no legacy processes, no existing team dynamics, and no clients expecting a traditional model. Everything you build is designed for AI-driven delivery from day one.
The Practical Startup Path
1. Choose 2-3 Initial Services
Do not launch with all nine services. Start with services where AI agents are most capable and where you have domain expertise. SEO + content marketing is a strong starter combination because both are highly automatable and in constant demand. Add web development if you have technical skills.
2. Build Your Agent Workflows Before Getting Clients
Create and test your agent workflows on your own business first. Write your own blog posts with content agents. Optimize your own SEO. Build your own dashboard. This gives you tested, reliable workflows and creates portfolio pieces that demonstrate your capability.
3. Set Up Your Tech Stack on Day One
A complete solopreneur tech stack in 2026 operates between €3,000-€12,000 annually. Get your AI subscriptions, hosting, automation platform, and payment processing configured before you start prospecting. The startup investment is minimal compared to traditional agency costs.
4. Price Competitively as a New Entrant
Consider starting at the Foundation tier (€2,000/month) to build your client base and portfolio. Your margins are still 60%+ at this price point. As you build a track record and referrals, scale pricing upward. The unit model makes price adjustments straightforward.
5. Overcome the Credibility Challenge
New agencies face the classic chicken-and-egg problem. The solution: use your own business as proof. Your website, your dashboard, your blog, your social presence — all built with the same agent workflows you sell to clients. Every asset you create for yourself doubles as a case study.
For Existing Agencies: The Transition Framework
Transitioning an existing agency to an agentic-first model requires a phased approach. The worst strategy is a sudden shift that disrupts client relationships and team morale. The best strategy is a gradual transformation where each phase builds capability and confidence for the next.
Phase 1: Internal Tool Adoption (Months 1-2)
Start by using AI tools for internal operations. Use AI for your own blog content, internal documentation, proposal writing, and project planning. This builds team fluency with AI tools without any client-facing risk.
- Every team member gets access to AI tools
- Weekly sharing sessions on what works
- Document internal use cases and time savings
Phase 2: Service Augmentation (Months 3-5)
AI begins assisting existing team members on client work. The human specialist still leads each project, but AI agents generate first drafts, conduct research, and handle repetitive tasks. Quality improves while delivery time decreases.
- AI generates first drafts; humans review and refine
- Track time savings per service
- Client deliverable quality should improve
Phase 3: Service Transformation (Months 6-9)
AI agents take the lead on execution. Human team members shift to verification, quality assurance, and client relationship management. This is where the economic model starts to shift — the same team can handle significantly more clients.
- Agents execute; humans verify and deliver
- Client capacity per team member increases 2-3x
- Begin testing unit-based pricing with new clients
Phase 4: Full Agentic Operation (Months 10-12)
The agency operates with a single orchestrator (or small core team) managing AI agents across all service areas. Existing team members have either transitioned to orchestrator roles, specialized in human-only functions (strategy, relationships), or moved on.
- Full unit-based pricing across all clients
- Client dashboard replaces account managers
- Margins reach 60-80% target
Communication with existing clients during transition is critical. Frame it as an upgrade: faster turnaround, more transparent tracking, data-driven optimization. Most clients care about results, not how many humans are involved in producing them. The clients who resist AI-driven delivery are typically the ones who will eventually switch to agencies that offer it at lower cost and higher speed anyway.
Limitations and What AI Agents Cannot Do (Yet)
Intellectual honesty about limitations builds credibility. The agentic-first model is powerful but not universal. Several categories of agency work still require significant human involvement, and some may remain human-led for years.
- High-stakes strategy: Brand positioning, market entry strategy, and competitive differentiation require human judgment and market intuition
- Complex client relationships: Navigating organizational politics, managing stakeholder expectations, and handling escalations
- Creative direction: Developing unique brand voice, visual identity concepts, and creative campaigns that resonate emotionally
- Crisis communications: Real-time reputation management and sensitive public messaging
- Video content creation: AI video tools like Seedance 2.0 and Sora 2 are advancing quickly but not yet production-ready for all use cases
- Complex negotiation: AI can draft proposals but real-time negotiation with nuanced responses is still human territory
- Cross-cultural marketing: Nuanced cultural adaptation beyond translation improves monthly but still needs human oversight
- Real-time community management: Monitoring and response automation is improving but contextual judgment gaps remain
The honest assessment: agentic-first does not mean human-free. It means human-efficient. You are still essential for everything that requires judgment, relationships, creativity, and accountability. The AI agents handle the execution-heavy work that used to require a team; you handle the strategic work that used to get squeezed by operational demands. Most agency owners find they do better work when freed from execution — they have time for the strategic thinking that actually differentiates their agency.
The Future of Agency Work
The agentic-first agency model is not hypothetical. It is being implemented now by solo founders and small teams across the digital marketing industry. Jacob Bank at Relay.app grew from zero to 1,000 paying customers using 40 AI agents and zero marketing employees. Agencies like Jellyfish reduced campaign launch times by 65% with AI agents. Superside deployed 40+ AI workflows and achieved 94% ROI for clients within six months.
The competitive advantage window is open but narrowing. Early movers capture market share by offering faster delivery, more transparent processes, and better economics than traditional agencies. The agencies that wait — the ones still debating whether to adopt AI — will face a market where clients expect agentic-level speed and pricing from every provider.
The democratization of marketing is the broader implication. Smaller businesses that could never afford a full-service agency at €10,000-€20,000 per month can now access comprehensive marketing services at €2,000-€4,000 per month. The agentic model expands the total addressable market while compressing the cost structure. More businesses get better marketing; the agency owner earns better margins. Both sides win.
Start with one service. Build your first agent workflow. Test it on your own business. Expand from there. The tools are available, the economics work, and the market is ready. The only question is whether you will build the agentic-first agency — or compete against one.
Build Your Agentic-First Agency
Whether you are starting from scratch or transforming an existing agency, we can help you design and implement the AI-powered infrastructure, client dashboards, and automated workflows that make the agentic-first model work.
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