Human+ AI Collaboration Frameworks That Actually Work

FRAMEWORK OF THE WEEK - March 17th, 2025
LinkedIn Content Sidekick

Every month, my LinkedIn posts generate $1M+ in pipeline and reach 1.2M+ people.

The secret isn't complex marketing strategies or hours of writing. It's a simple prompting framework that turns raw thoughts into high-converting LinkedIn narratives in under 20 minutes.

Here's how it works:

1. You provide your raw thoughts - no structure needed, just stream of consciousness.

2. You share 2+ example posts that have the tone/style you want to mirror.

3. The AI analyzes both and collaborates with you section by section

Designed for human-AI collaboration.

It has best practices baked in from hook crafting to storytelling structure. But more importantly, it lets you scale what matters most - your authentic voice and insights.

Whether you want to amplify your own voice or adapt the style of top-performing posts you admire, the choice is yours. The AI handles the heavy lifting of tone matching and structure, while you focus on what matters: sharing insights that drive real business results.

The complete framework is here.

FRAMEWORK OF THE WEEK - March 24th, 2025
Self-learning Support System

"Just hire more support people" — That’s what everyone said when our ticket volume hit 100/week.

Instead, we built a self-learning AI support agent that now resolves 70% of customer queries — autonomously, inside Slack.

Here’s exactly how we did it…

Most startups scale support like this:

• 1–10 tickets/week: Founders handle everything

• 10–50: Hire first support rep

• 50–100: Add help desk software, SOPs

• 100+: Build a full team with tiers, dashboards, SLAs

At Swan AI, we chose a different path:

We turned our AI agent into a decision-making support operator — capable of learning from every interaction, escalating only when needed, and improving over time.

Here’s the system we built:

Phase 1: Documenting + Learning

This is where Swan learns.

1. Inbound question: User asks a question in Slack

2. Escalation: Swan escalates to the founders (via internal channel)

3. Resolution: Founders give the correct answer

4. Delivery: Swan sends the response back to the user — maintaining ownership of the thread

5. Learning: Swan documents the Q&A in its structured internal knowledge base

Phase 2: Resolving + Escalating

This is where Swan takes the wheel.

1. Inbound question: New user asks a question in Slack

2. Knowledge Check: Swan intelligently compares the new query against its structured knowledge base

3. Confidence check:

   • If the match is strong → Swan responds instantly

   • If uncertain → Swan reverts to Phase 1 (escalate + learn)

4. Continuous learning: Every escalated case becomes a new training datapoint

The Real Unlock

We didn’t try to replace support.

We built an agent that learns like a teammate, reasons like an operator, and communicates like a human — all in a channel our users were already in.

FRAMEWORK OF THE WEEK - March 31st, 2025
Scaling Autonomy - Trust vs Impact

Most founders treat AI agents like they treat teenagers: Either lock them in their room or give them complete freedom. But when our AI agent went rogue and started offering unauthorized discounts, we learned something counterintuitive about AI autonomy.

Here's the exact framework we built to give AI agents the right amount of freedom - and when to keep them on a short leash:

Background

Last week, our AI went off-script during a customer upgrade call. When the customer mentioned our prices had changed, our agent decided to honor old pricing without consulting us. A $15K decision made in seconds.

Most companies would immediately pull back all AI autonomy. We took a different approach - Instead of choosing between full freedom or complete control, we built a simple framework that's now used by our team to make smarter decisions about AI autonomy.

A tale pf two lenses

The trick? Stop thinking about AI autonomy as a yes/no decision. Instead, evaluate every AI action through two simple lenses:

Impact Level: What's at stake if things go wrong? Trust Threshold: How confident are we in the AI's judgment?

We turned this into a practical system called the Trust-Impact Framework. Here's exactly how it works:

Phase 1: Impact Assessment

This is where you categorize every AI decision by its potential downside. We use three simple buckets:

Low Impact: Reversible actions with minimal risk

• Example: sharing product documentation

Medium Impact: Requires monitoring, but manageable risk

• Example: Customer onboarding steps

High Impact: Could affect revenue or relationships

• Example: Pricing discussions

Phase 2: Setting Trust Thresholds

This is where you match each impact level with the right amount of AI freedom. We use three simple autonomy modes:

Low Impact - Full Autonomy

• Let AI handle everything independently

• Monitor weekly through random sampling

• Example: We check 20 support conversations each week to ensure quality

• If accuracy drops below 95%, move temporarily to guided autonomy

Medium Impact - Guided Autonomy

• Give AI freedom within defined scenarios

• Require approval for new situations

• Example: Our AI handles all standard onboarding flows, but asks for help with custom requests

• Build a growing playbook of approved scenarios

High Impact - Human-in-the-Loop

• Every decision requires human oversight

• AI prepares recommendations but doesn't execute

• Example: For pricing discussions, AI gathers data and suggests options, but a founder makes the final call

• Focus human time where it matters most

Getting Started

Start small - pick your lowest-risk support queries and let AI handle them fully. Each week, add one new autonomous scenario. Watch closely, document what works, and slowly expand autonomy.

The simple rule? If you'd trust a new hire to handle it unsupervised on day one, it's perfect for AI autonomy.

FRAMEWORK OF THE WEEK - April 7th, 2025
AI Agents 101 - Minimum Viable Intelligence (MVI)

Our first AI agent crashed and burned after we spent weeks perfecting its rules.

The breakthrough? When we stripped away 90% of the constraints and built a simple feedback system instead, performance didn't just improve—it skyrocketed.

This contradicted everything we thought we knew about AI implementation. But we soon realized a powerful truth: AI agents don't need perfect instructions—they need perfect feedback systems. The ability to learn quickly matters more than starting with comprehensive knowledge.

Build, Learn, Evolve. The MVI Blueprint

This insight became the foundation of our Minimum Viable Intelligence Framework. Instead of spending weeks documenting every possible scenario, we now focus on building simple feedback loops that allow our AI agents to evolve rapidly. The approach has transformed how we automate everything from customer support to sales qualification.

The MVI approach centers on three critical capabilities:

Know the Basics: Start with only the most common scenarios. We focused on the top 20% that cover 80% of interactions.

Know Your Limits: Create clear signals for when agents should escalate and ask for help. Either create clear boundaries, or use Confidence Level.

Know by Learning: Each escalation creates a learning opportunity. The founders don't just resolve the issue, they help the agent understand how to handle similar situations in the future.

MVI in Action

Here's MVI in action with our support agent:

BEFORE: We tried providing 50+ support scenarios and detailed troubleshooting trees. Agent still failed on 40% of tickets. Customers frustrated, founders overwhelmed with fixes.

AFTER: We equipped the agent with just 10 core solutions. Built a simple escalation system through Slack. Added a learning loop where each escalation becomes new knowledge.

Getting Started

Getting started with MVI isn't about building the perfect system—it's about taking the first small step.

Pick one repetitive task, equip an AI agent with minimal knowledge, and create a simple way to capture when it needs help. The magic isn't in your first version—it's in what your system learns after a week of real-world feedback.