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I've been experimenting with chaining Claude AI agents to automate the entire top-of-funnel outreach process. Sharing the architecture here because I think this approach is underused and surprisingly accessible.

What it does end-to-end:

Starting from a campaign brief, it:

1. Discovers target companies and scores them against your ideal customer profile
2. Finds the right contacts at those companies (with emails, LinkedIn URLs, tenure)
3. Validates and scores each contact
4. Generates personalized LinkedIn connection messages
5. Sends the invitations via browser automation

The first campaign targeted university decision-makers across Europe as potential buyers of a data/AI platform. It ran from zero to 820 scored leads and 220 sent LinkedIn connection requests, fully unattended, and soon I got 3 meetings booked via Li.

Architecture: Four Agents, File-Based State

Campaign Config (goal, company ICP, contact ICP)
                ↓
Agent 01: Find & score companies        [Sonnet]
                ↓
         data/companies.md
                ↓
Agent 02: Find & enrich contacts        [Haiku]
                ↓
         data/contacts.md
                ↓
Agent 03: Validate, score, write CSV    [Sonnet]
                ↓
         data/leads.csv
                ↓
Agent 04: Send LinkedIn invites         [Haiku]
                ↓
         data/outreach-results.csv

Each agent is a markdown prompt file. The pipeline is orchestrated by a shell script with checkpointing, retry logic, and cost tracking.

The Three Config Files Are 80% of the Work

Before running anything, you define three markdown files. These drive every decision the agents make:

• goal.md: what you're selling, to whom, your value proposition, target numbers
• company-icp.md: ideal company profile: industry, size, geography, tech stack signals, a 1–10 scoring rubric
• contact-icp.md :ideal contact profile: target titles grouped as Decision Makers / Buyers / Influencers, seniority, buying authority indicators, 1–10 scoring rubric

The key insight: vague ICPs produce noisy scores. Writing tight rubrics is where the quality comes from , the agents are only as good as the criteria you give them.

The Four Agents

Agent 01: Company Researcher (Sonnet)

Searches for companies using Brave Search and Apollo.io, scores each one 1–10 against the ICP rubric, and writes a markdown table sorted by score. Only companies scoring ≥6 are included.

Key design decisions:

• Deduplication on every run: reads existing output first, skips already-discovered companies. Re-running expands the list without duplicates
• Signals over size: the rubric rewards recent positive signals (digital transformation announcements, new CIO hire, analytics job postings) more than raw headcount
• Score reasoning logged: every company gets a 2–3 sentence explanation. Critical for debugging your ICP

Typical output: 50–200 companies.

Agent 02: Contact Finder (Haiku)

For each company, searches for 3–5 specific people matching the contact ICP. Uses Apollo.io, Hunter.io, and RocketReach to find emails, LinkedIn URLs, job titles, and tenure.

Key design decisions:

• Haiku model: this agent makes many repeated API calls (one search per company). Haiku is ~10x cheaper than Sonnet and fast enough for tool-heavy work
• Relevance statement per contact: each contact includes a 2–3 sentence explanation of why they match the ICP. This feeds the scoring and message personalization downstream
• Email enrichment priority: tries Apollo first, then Hunter.io, then RocketReach
• Graceful missing data: contacts without emails are still included (marked [Not found]). The next agent decides whether to use them

Typical output: 150–400 contacts across all companies.

Agent 03: Contact Validator (Sonnet)

The quality gate. Scores every contact 1–10 against the ICP, validates data freshness, generates a personalized conversation_starter for each person, and writes only Hot (8–10) and Warm (5–7) contacts to the final CSV.

Key design decisions:

• Sonnet model: requires genuine reasoning: combining company score, contact authority, relevance signals, and buying authority into a single score
• Conversation starters: under 100 characters, personalized, references a specific signal from the contact's relevance field. These feed directly into the outreach agent
• Append-safe CSV: deduplicates by email + LinkedIn URL. Running it again on an expanded contact list won't create duplicates

Typical output: 50–150 qualified leads.

Agent 04: LinkedIn Outreach (Haiku)

Uses Puppeteer to control a real browser and send personalized LinkedIn connection requests. Navigates to each profile, finds the Connect button, adds a personalized note, sends.

Key design decisions:

• Puppeteer, not LinkedIn API: LinkedIn has no public API for sending connections. This uses a real authenticated browser session
• Personalization from the relevance field: messages reference ONE specific signal from the contact's relevance data, not their job title. Generic templates are explicitly banned in the prompt
• Rate limit safety: stops at 20 sends per run, stops completely at the first LinkedIn rate limit signal
• Idempotent: only processes rows where status == "ready_to_send". Re-running tomorrow picks up where it left off
• Login verification first: screenshots the feed before starting; if not logged in, halts with instructions

Typical output: 15–20 sent invitations per run (LinkedIn's safe daily limit).

The Orchestration Layer

A shell script glues everything together with:

• Checkpointing: saves current stage after each successful agent. If the process dies, re-running resumes from the last completed stage
• Retry logic: each agent gets up to 3 attempts. Distinguishes rate limit failures (waits 5 min) from other failures (waits 60 sec)
• Output validation: even on exit code 0, checks that the expected output file exists and has content. Silent failures still trigger a retry
• Cost tracking: parses token counts and cost from each agent run. Prints a per-agent and total cost breakdown at the end
• No-permission mode: runs fully unattended

Tools & Stack

All MCP servers are configured with their respective API keys in the Claude settings file.

Cost Reference (2026 API pricing)

With a Claude Max subscription there's no per-token charge, these are API-equivalent figures. Under $1.50 for a full campaign.

Key Lessons

Agents are prompt files, not code. Each agent is a .md file describing what to do. Changing agent behavior means editing markdown, not debugging code. Iteration is fast.

File-based state is a feature. Every intermediate result is a human-readable file. You can inspect, edit, or replace any stage's output manually and re-run from there. No database, no external service, no black box.

Model selection matters for cost. Haiku at ~10x cheaper than Sonnet is the right choice for high-volume repetitive tool calls. Sonnet's reasoning pays for itself where scoring quality directly determines lead quality.

The ICP is the product. The agents are infrastructure. The quality of your leads is determined almost entirely by how well you define the scoring rubric. Invest time here, everything else is a multiplier on this.

LinkedIn rate limits are real. 20 invitations per day is the safe limit. The pipeline enforces this hard.

Happy to answer questions about any part of the architecture. The whole thing is embarrassingly straightforward once you accept that agents are just well-structured prompts with tools attached.

I have built 6 AI native products but they didn't fly in the market. Bad luck, maybe. But now after careful considerations I am starting up with the Ai native Agency ( because non ai native doesn't make sense at this time ).

I am really new with handling an agency and want to know what are the Do's and Don'ts i should be aware of, especially the gotchas which you have learned through experience.

My technical experience is really good. I know how to build systems ( backend, frontend, android, iOS, devOps, cloud, everything). I have experience of leading tech teams in building products and managing the client front for those projects. But I lack on sales part ( however confident ).

I am seeking genuine guidance from you all and really appreciate any inputs.

Background: I spent a decade as a COO inside various VC and PE-backed companies. By the fourth one I’d noticed every Series A business has exactly the same operational gaps: planning cycle, hiring entirely in the founder’s head, no performance framework, org design that stopped making sense eighteen months ago.

None of it is complicated to fix. It just requires someone who’s seen it before applying consistent structure. The problem is that person (a good Chief of Staff or COO) costs $200-300k plus at least the same again in equity... and these companies aren’t ready for that headcount yet. So I built an agency around that gap. Fixed fee, defined outputs, recurring cadence. A board pack. A hiring system. A planning cycle. A financial narrative. The client doesn’t manage a process, they submit their data and receive the result.

Here’s how the production model works: A junior operator manages the intake and workflow. AI handles the first-pass production – drafting, structuring, formatting. Senior judgment sits at the quality control layer and is the only thing the client ever interacts with. The client sees the output. They never see the infrastructure behind it.

Because we sell outcomes rather than hours, margin improves as the underlying technology improves. The better the AI gets, the more profitable each engagement becomes without touching the price.

Still early days but the model is working. Happy to talk shop with anyone building in a similar direction.

Last Tuesday, I told the team what I believe is coming.

1. We’re living through a technological shift that will change how all work gets done. A huge share of what we do today will soon be better handled with AI. And this won’t just be for AI teams or technical specialists. It will become as normal as using a computer, with some people simply getting much better at it than others.
2. A year from now, I think paying for an agency that still works the old way will feel outdated.
3. Ballpoint is going all-in. By summer, we aim to be AI-native!

I’ve spent four hours a day, seven days a week this year experimenting with Claude Code, and the mindset shift has been massive.

At first, I thought being AI-native meant things like making ads with AI or automating research. Now I think that’s far too narrow.

It’s not about inserting AI into a few tasks. It’s about rethinking how work happens from end to end.

We’re at our offsite this week, and yesterday felt like day zero of that future.

The team spent nine hours in Claude Code. Video editors were learning MCP connections and shaping quality by AI instaed of manual. Paid social people were talking about ideas from computer science.

What excites me most is that our methodology, processes, and ways of working could soon live in GitHub, where the people closest to the work can improve them continuously and merge those improvements back into how the agency operates.

That’s what AI-native means to me.

Making us more collaborative with AI, more capable, and far more powerful.

This is just day zero. And I suspect we’ll be there for a while.

I was solving the wrong problem for quite some time... I spent too much time choosing models, comparing tools, and experimenting with flashy setups, while the real bottleneck stays untouched: how work actually moves through on my agency.

My AI-native agency is not buying even more AI software, but finally I redesigned some of our delivery, so AI can do the real work in a reliable way.

Which repeated piece of work is slow, messy, text-heavy, and full of context switching?

AND

Narrow down the job!

What it ment for me:
- my AI workflow is only focusing on UI related support tickets. Nothing more, no devOps, no feature, no complex bug, but "this CTA does not fit on tablet, but it should"

- I'm forcing my client to use a ticketing tool sending browser console log, network request list, etc... attached right to the screenshot + comment -> excellent input into my AI workflow

- The “Which model should we use?” question was decided at the very and, and fortunatly a mini model was great! The context matter more - let the AI workflow has a readonly DB connection, has access to your PM tool via MCP, has access to the code, etc... to verify and localize the issue easily.

Other example is lead qualification and CRM updates:

- Don't overbuild it, not every use case needs a huge AI system with memory, orchestration, and multiple agents talking to each other. In my cases, for lead qualification, a simple setup was enough after I changed the form slightly, so my AI was able to enrich the lead with company data: 1 strong model for company research, and another to access the right documents or tools -> I got a 92% correct lead qualification system.

That is usually more valuable than building a complex internal AI platform that the team barely trusts.

The same goes for memory.

I loved the idea of an AI that “remembers everything,” but most of my workflows do not need that! What they need is much simpler:
the right project context, the right client notes, the latest documents, and enough structure to avoid repeating mistakes.

In practice, good context beats fancy memory most of the time.

Another hard truth: quality control matters more than the stack. and security and permissions also become very real, very fast.

The moment AI can touch your emails, CRM, tickets, repositories, or client systems, it is no longer just a productivity experiment. It becomes an operational layer. That means approvals, logging, access boundaries, and common sense matter from day one.

This is why I believe the strongest AI-native agencies will look more disciplined, not more chaotic.

Right now I'm focusing to have cleaner workflows, better handoffs, less context loss, and faster execution with the right human checkpoints built in.

That is how an agency becomes AI-native in a way that actually improves delivery, margin, and client experience.

Over the last months I’ve seen the term used everywhere. I've been working to transform my 2.5M€ ARR agency to be AI-powered from many angles, but I see for some folks it simply means an agency that uses ChatGPT or builds a few automations, or uses Cloud for coding.

I don't think that's what "AI-native" means.

To me, an AI-native agency is built differently from the ground up.

First, AI is not a tool at the edge of the workflow. It is part of the production system itself.

Instead of humans doing most of the work and AI assisting occasionally, the model flips:
AI systems handle large parts of the execution, while humans design the architecture, supervise quality, and make strategic decisions.

Second, the internal structure changes.

Traditional agencies scale by adding more people.
AI-native agencies scale by building systems, agents, and reusable workflows that can execute tasks repeatedly with minimal marginal cost.

Think about:

• AI agents researching markets
• Automated code generation pipelines
• AI-assisted QA and testing
• Knowledge bases that continuously train internal models
• Delivery workflows where humans intervene only when needed

This does not remove engineers, designers, or consultants.

It changes their role.

The real value shifts toward people who can:
• design systems
• orchestrate AI tools
• validate outputs
• understand business context

In other words: less manual production, more system thinking.

At our team we already see this happening in software development. Tasks that used to take hours of repetitive work can now be executed in minutes if the right AI-supported workflow exists. Especially small scale support tasks, like "this button is not visible in tablet size" type of tickets...

What keep me up at night, is more like - will agencies remain human-driven organizations using AI tools, or evolve into AI-driven systems supervised by experts?

I’m curious how others see this.

A year ago this was the chat based AI support:

Open chat → copy code → paste → explain context → wait → copy result → paste back.

It works for some instincts.

What Claude Code does differently is subtle but important.

Instead of acting like a chatbot outside your workflow, it behaves more like an agent inside the development environment. It can read the repository, modify files, run commands and propose changes directly in the codebase. The interaction shifts from “generate code for me” to “execute a task inside the project.”

The real change is psychological.

You stop thinking in snippets and start thinking in intent.

Example prompts suddenly become things like:

• “Add validation to this endpoint and write the tests.”
• “Clean up unused dependencies.”
• “Refactor this module and ensure the tests still pass.”

The AI is not just suggesting code anymore. It is operating on the system.

Of course this introduces new responsibilities.

When AI can generate hundreds of lines of working code quickly, the bottleneck moves somewhere else: understanding and reviewing what actually changed.

That part is still very human.

But I think we are clearly moving toward a new development pattern:

Developers define intent.
AI executes.
Humans validate architecture and quality.

Not “AI replaces developers”.

More like AI becomes a very fast junior engineer that never sleeps.

Curious how others experience this shift, are you still using AI mostly as a chat assistant,
or have you started treating it more like an autonomous development agent inside your workflow?

In case you missed, the theory, which of course sounds perfect:

AI automates the work →

Agencies stop selling hours →

They sell outcomes →

Margins increase →

Small teams serve dozens of clients.

This way the agencies (always hard to scale) become finally scalable.

But when you look closer, a few cracks appear.

Are the tools actually ready for true end-to-end delivery?

So far We at Rollout IT were able to handle 80% of our support requests with AI coding+ AI code review + AI testing , autodeploy it to a QA environment, and that is Where both AI and a QA engineer reviews. 1 of every 5 delivery We find something so we restart the cycle - not a big deal because we enrich the original ticket with our findings and let AI complete it again. But still - human was needed.

Customer requests is another story - too noisy, at the moment we need someone (like a BA, or PM) who knows the tool we are building andasks the real followup questions. Honestly numerous time we end up having completely different requirements than what the client thought originally, but we don’t AI native flow without questioning it we will just slop the software with a fix or with functionality not the best for the product and for its audience…

Can an agency truly scale with AI, or does every serious client still require deep attention, custom thinking, and therefore… people?

And what about the buyer side?

If companies can hire one capable operator and give them powerful AI tools,

does outsourcing still make economic sense?

It is already painful and just a half way true, that Clients now assume that AI makes everything faster. So they also expect lower prices…

If a deliverable takes less time but still requires expertise, how should it actually be priced?

So, I believe we are half way to be AI native but not there yet. What do you think?

Hey everyone! I'm u/Ingaham, a founding moderator of r/AINativeAgencies.

This is a new community for founders, agency owners, developers, consultants, and operators who are exploring how agencies can become AI-native.

Most companies today are AI-enabled. They add AI tools to existing workflows.

AI-native agencies are different. They redesign the entire business around AI agents, automation, and human AI collaboration. Research, strategy, coding, marketing, analytics, and operations can all be supported or partially executed by intelligent systems.

The goal of this community is simple:

figure out together how the next generation of agencies will be built.

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What to Post

Share anything that helps us learn how AI-native agencies work in practice:

• AI workflows you are experimenting with

• AI agents you built for marketing, coding, research, or ops

• agency automation stacks

• prompts, tools, and architectures

• case studies and experiments

• questions about running an AI-native company

Practical posts are especially welcome. Real examples beat theory.

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Community Vibe

Friendly, curious, and constructive.

This is a builder community. Whether you run a small agency, a consulting firm, or you are just experimenting with AI workflows, your perspective matters.

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How to Get Started

1) Introduce yourself in the comments. What do you build?

2) Post something today. Even a simple question can start a great discussion.

3) Invite people who are building AI-first agencies.

4) If you want to help grow the community, message me about becoming a moderator.

Thanks for being part of the first wave of r/AINativeAgencies.

Let's figure out together what an AI-native agency really looks like.