Evaluating AI Agents: A Framework for Non-Technical Founders

How to Choose the Right Conversational AI Platform (And Why Most Operators Are Choosing Wrong)

If you're running customer operations, you've probably been pitched AI agents by a dozen different vendors in the last six months.

They all promise the same thing: "Automate your customer service. Cut costs by 80%. Deploy in days."

But here's what I've learned working with hundreds of operations leaders: Not all AI agents are built the same. And the difference is about fundamental architecture.

Last week, I talked to a CEO who spent three months building conversational AI on Zapier. He finally got something working, but it broke every time a customer asked anything outside the script. He was drowning in edge cases and ready to give up on AI entirely.

Four weeks after switching to Conduit, he automated 68% of his conversations.

The problem wasn't his effort. The problem was that he chose a tool built for general automation and tried to force it to handle dynamic conversations.

Here's the honest evaluation framework I wish someone had given him before he invest time and money into conversational AI.

Why Evaluating AI Agents Is So Confusing

If you're business minded, you've probably noticed that every AI agent vendor shows you the same perfect demo.

The AI handles every conversation flawlessly. It never gets confused. It routes to humans seamlessly.

But when you implement in production, reality hits hard.

The AI struggles with edge cases. Integration takes weeks. Your team spends more time fixing the AI than it would take to just answer the questions themselves.

This happens because most platforms are general-purpose tools trying to handle a specialized job, or human centric help desks with AI bolted on as an afterthought.

It's like the difference between renovating an old building and building new from the ground up. Sure, you can retrofit modern plumbing into a 100 year old house. But it'll never work as well as a house designed with modern plumbing from day one.

The AI Agent Spectrum: Understanding Your Options

After implementing conversational AI across industries, I've identified four categories of tools. Each has different architectural foundations that determine what they can and can't do.

Here's the spectrum, from most flexible to most specialized:

Category 1: Flexible No-Code Builders (Zapier, Make, N8N)

What They Are:

Horizontal automation platforms that let you build any workflow, beyond just conversational agents.

Where They Excel:

• Document processing and OCR

• CRM enrichment

• Data mapping across tools

• One off automation tasks

Why They Struggle for Conversations:

Here's what most people don't realize until they're 40 hours into implementation: these tools weren't designed for dynamic conversations.

To build even a basic Q&A bot, you need to:

• Set up your own database architecture

• Build CRM data models from scratch

• Create contact deduplication logic

• Design your own escalation workflows

• Build an inbox (because these platforms don't have one)

One operations leader spent 40 hours building a "simple" FAQ bot. It worked for basic questions, but when customers asked follow ups or changed topics mid-conversation, everything broke down.

The Core Issue:

These platforms treat conversations as linear workflows with predictable inputs and outputs. But real conversations are messy, contextual, and require constant adaptation.

When to Use Them:

For general business automation across your company. Not as your primary conversational AI platform.

Hidden Cost Reality:

• Low monthly fee ($20-100/month)

• But 40-80 hours of technical work to build basic features

• Ongoing maintenance: every edge case requires custom logic/code

Category 2: Headless AI Agents (Sierra, Decagon)

What They Are:

AI agents that plug into your existing help desk (Zendesk, Salesforce) and work in the background.

When They Make Sense:

If you already have 20+ agents on Zendesk or Salesforce, these should be your default choice. The switching cost of moving platforms isn't worth it, and you'll see solid incremental gains with minimal disruption.

The Architectural Constraint:

Here's the fundamental challenge: these AI agents are retrofitted into help desks that were built for humans, not AI.

Your Zendesk system was designed around human workflows, human ticket routing, and human UI patterns. When you plug an AI into that system, you're treating it like a "back office worker" who you check in with once a week.

You might update the knowledge base, tweak some prompts, or adjust routing rules. But the AI isn't sitting next to you as a colleague. It's working in the background while your team works in the foreground.

The Iteration Speed Problem:

I worked with a Director of Support using a headless AI. When she wanted to teach the AI something new:

1. Update the knowledge base documentation

2. Wait for the next sync cycle (usually weekly)

3. Hope the AI picked it up correctly

4. Monitor tickets for a week to see if it worked

When she discovered an edge case the AI was mishandling, it took 10 days to fix because the feedback loop was so slow.

Automation Ceiling:

Headless agents typically cap out at 40-60% automation because they're constrained by the underlying help desk architecture. The system simply wasn't designed to enable deeper automation.

When to Use Them:

If you have a large team already on Zendesk or Salesforce and the switching cost is too high.

Cost Reality:

• Enterprise pricing ($2,000-10,000+/month)

• 2-4 weeks implementation

• Moderate ongoing maintenance (knowledge base updates, prompt tuning)

• 40-60% automation ceiling

Category 3: All-in-One AI Help Desks (Intercom FinAI)

What They Are:

Help desks with built-in AI that were designed for human agents first, with AI added later.

When They Make Sense:

If your team already uses Intercom, just use FinAI. It's the most straightforward approach with native integration and minimal migration headaches.

The Same Architectural Problem:

Just like headless agents, you're plugging AI into a system that was architecturally designed for humans. Intercom's interface, workflows, and data models were all optimized for human agents first.

The AI is powerful, but it's fundamentally constrained by those original design decisions.

Specific Limitations:

• Limited customizability (boxed into Intercom's templates)

• Difficult to implement complex multi-step sequences

• Similar "back office worker" dynamic as headless agents

• Hard to build custom workflows outside their molds

When to Use Them:

Stick with FinAI if you're already an Intercom customer.

Cost Reality:

• Mid-to-high pricing (starting around $99/month + per-seat + AI add-ons)

• Fast implementation (days to weeks)

• Moderate customization limits

• Similar 40-60% automation ceiling

Category 4: Native AI-First Platforms (Conduit)

What Makes This Different:

This is where I need to be transparent: I built Conduit because I saw the architectural constraints of every other category.

The fundamental question we asked was: What if we designed the AI and the inbox together from the ground up, treating AI as a colleague rather than a tool?

That architectural decision changes everything.

The Core Difference: Real Time Learning

In every other category, teaching your AI is a batch process:

1. Something breaks

2. You update documentation

3. You wait for sync cycles

4. You hope it learned

With Conduit, your AI sits next to you in the same interface. When you see it struggle, you click "teach the AI" and give it instructions in natural language. The learning happens in real time, not in batch cycles.

Why This Matters in Production:

One VP of Customer Experience I worked with reduced her team from 30 agents to 6 (80% headcount reduction) using Conduit.

She runs a "daily edge case review", which takes 15 minutes each morning where she teaches the AI how to handle situations it struggled with yesterday.

That rapid iteration compounds quickly. She went from 65% automation to 89% automation in 30 days just by tightening this feedback loop.

With headless or all-in-one systems, that same improvement would have taken 6+ months because of the slow batch learning cycle.

The Context Advantage:

Her AI now resolves 73% of conversations end-to-end because it has instant access to context no human agent could practically pull up:

• Complete customer history across all channels

• Purchase data and account status

• Previous issues and resolutions

• Internal business rules and policies

• Real-time inventory or availability data

In traditional help desks, human agents have maybe 2-3 minutes to pull this context. They usually just wing it based on the ticket in front of them.

With native AI-first architecture, that context is automatically available in every conversation.

Granular Control That Actually Matters:

Here's a real-world constraint that breaks most systems: temporary situations.

What happens when you have a partial outage or a temporary policy change?

With most platforms, you update the knowledge base, and that information lives there forever. You don't want the AI to remember temporary situations permanently.

Conduit lets you set timers on knowledge. You can tell the AI: "For the next 4 hours, if customers ask about X, tell them Y because we have a temporary issue." After 4 hours, that knowledge expires automatically.

One operations leader manages a property management company with frequent maintenance windows. She sets temporary instructions for the AI during outages and they automatically expire when the issue is resolved. This level of operational control is simply impossible with batch-learning systems.

The Automation Ceiling:

This is the most important difference: Conduit customers typically reach 75-90% automation because the architecture was designed for AI from the start.

Other platforms cap out at 40-60% because they're constrained by human centric design decisions.

That 30 point difference isn't marginal. It's the difference between reducing your team by 60% or reducing it by 90%.

Real Customer Outcomes:

→ 80% headcount reduction (30 agents to 6) while improving CSAT

→ 73% of conversations resolved end-to-end with no human involvement

→ Response times: 3.2 hours down to 8 minutes

→ 65% to 89% automation in 30 days through daily edge case teaching

→ One mortgage lender booked $600k in pipeline in 2 weeks through AI-powered lead qualification

When Conduit Makes Sense:

1. You're not locked into an incumbent help desk

If you're not already on Zendesk or Salesforce with a large team, you don't have the switching cost problem. You can choose the platform with the highest automation ceiling from day one.

2. You want maximum automation, not just incremental gains

If your goal is to reduce headcount by 60-80% and fundamentally transform operations, you need architecture built for that outcome.

3. You handle dynamic, complex conversations

If your conversations involve multiple steps, context from various systems, or frequent edge cases, native integration matters.

4. You're willing to invest in proper implementation

Conduit takes 2-4 weeks to implement properly. That's longer than some alternatives, but the automation ceiling is dramatically higher.

When Conduit Doesn't Make Sense:

Be honest with yourself about these constraints:

• You have 20+ agents already on Zendesk/Salesforce (switching cost too high)

• You want the absolute easiest path to "some" automation

• You're not willing to invest 2-4 weeks in proper setup

• You only need to automate simple FAQ-style conversations

Cost Reality:

• Mid-market pricing ($500-2,000+/month depending on volume)

• 2-4 weeks for first workflow (faster for subsequent ones)

• 15-30 minutes per week ongoing maintenance (real-time teaching vs. batch updates)

• 75-90% automation potential

The Decision Factors That Actually Matter

After walking hundreds of operations leaders through this evaluation, here's what determines success:

1. Time-to-Value (Not Just Implementation Time)

Most vendors talk about "time to deploy." That's the wrong metric.

What matters is time-to-value: How long until the system is delivering measurable ROI?

Real Comparison:

One COO implemented a headless AI in 5 days but took 8 weeks to reach 40% automation because every edge case required updating documentation and waiting for sync cycles.

Another implemented Conduit in 14 days but hit 68% automation by week 4 because the real-time teaching loop accelerated learning.

Slower deployment, faster value.

2. The Automation Ceiling

The question isn't "Can this automate 50% today?"

It's "What's the maximum this system can automate over time?"

Headless agents cap at 50-65% because of architectural constraints.

Native platforms like Conduit reach 75-90% because the architecture was designed for AI from day one.

That difference compounds. After 6 months, you're either reducing headcount by 60% or 90%. Both save money, but one transforms your business.

3. Edge Case Iteration Speed

Conversational agents are perfect in demos. In production, you discover dozens of edge cases you never anticipated.

The critical question: How fast can you fix them?

• Slow (weekly): Headless agents, knowledge base systems

• Medium (daily): All-in-one systems with batch learning

• Fast (real-time): Native platforms with inline training

One operations leader told me: "With our old system, fixing an edge case took 10 days. With Conduit, it takes 10 minutes. That's not 10% faster. That's a fundamentally different operational model."

4. Total Cost of Ownership

The monthly fee is only part of the cost.

Real Cost Breakdown:

Option A: Zapier

• $50/month subscription

• 60 hours implementation ($3,000 in labor)

• 5 hours/week maintenance ($1,300/month ongoing)

• Total first year: $18,650

• Automation achieved: 30-40%

Option B: Headless Agent

• $5,000/month subscription

• 20 hours implementation ($1,000 in labor)

• 2 hours/week maintenance ($500/month ongoing)

• Total first year: $67,000

• Automation achieved: 50-60%

Option C: Conduit

• $1,500/month subscription

• 30 hours implementation ($1,500 in labor)

• 30 minutes/week maintenance ($125/month ongoing)

• Total first year: $21,000

• Automation achieved: 75-90%

The highest automation at lower total cost because of architectural efficiency.

A Practical Decision Framework

Here's how to choose:

If you have 20+ agents on Zendesk or Salesforce:

→ Use Sierra or Decagon

→ Accept 50-65% automation as a reasonable trade-off

→ Switching cost probably isn't worth it

If you currently use Intercom:

→ Use Intercom FinAI

→ Lowest friction path

If you're starting from scratch or willing to switch:

→ Consider Conduit seriously

→ You're choosing architecture, not just features

→ The automation ceiling difference (30+ percentage points) compounds over months

→ Real-time learning vs. batch learning changes everything

If you need general automation (not conversations):

→ Use Zapier or Make for workflows

→ Don't try to build conversational AI here

The Questions to Ask Every Vendor (Including Us)

Cut through the marketing with these questions:

1. "What's the highest automation rate your customers have achieved, and how long did it take?"

• We'll show you customers at 75-90% and the timeline to get there

• Ask competitors for the same specifics

2. "Show me how I teach the AI to handle a new edge case right now."

• Watch whether it's real-time or batch updates

• With Conduit, you'll see the "teach the AI" button and natural language training

3. "What happens when a customer changes topics mid-conversation?"

• This reveals true contextual understanding vs. keyword matching

• Most demos don't show this scenario

4. "Walk me through your last customer who didn't succeed. What happened?"

• We'll be honest: customers who want 90% automation in week 1 don't succeed

• Those who invest 2-4 weeks in proper setup hit 75%+ by month 2

5. "What's your automation ceiling, and what determines it?"

• We'll explain why our native architecture enables 75-90%

• Ask competitors why they cap at 50-65%

What We Got Wrong (So You Learn From It)

I'll be honest about our own journey.

Early on, we thought the most important thing was building the smartest AI possible. We obsessed over prompt engineering and model selection.

We were wrong.

What actually matters is the feedback loop between the AI and the humans training it.

The fastest path to high automation isn't the perfect AI on day one. It's the AI that learns fastest from real production conversations.

That's why we built Conduit as a native AI+inbox platform where the AI sits next to you as a colleague, learning in real-time as you work together.

That architectural decision is the reason our customers hit 75-90% automation while other platforms cap at 50-65%.

Making Your Decision

If you're running operations, you know the most dangerous decision is the decision made without enough context.

Here's what I'd recommend:

If you're locked into incumbent systems with large teams:

Stay put. Use the AI that integrates with what you have.

If you're starting fresh or willing to switch:

You have a rare opportunity to choose architecture, not just features.

The question is: Do you want to optimize for ease of adoption or automation ceiling?

Ease of adoption → Stick with what you have

Automation ceiling → Choose Conduit

The difference between 60% and 85% automation might not sound huge. But it's the difference between reducing your team by two-thirds or reducing it by 85%.

After 12 months, that difference is measured in millions of dollars and hundreds of hours.

See It In Action

The best way to understand the difference is to see it.

I'm happy to show you:

• How real-time teaching works in production

• Customer examples in your industry

• The specific automation ceiling you could reach

• Honest assessment of whether Conduit is right for your situation

Even if Conduit isn't the right fit, I'll tell you which category is.

Book a demo: punn@conduit.ai

The technology is ready. The question is which architecture aligns with your goals.

About Conduit

Conduit (YC W24) is built for operations leaders who need production-ready conversational workflows with the highest automation ceiling in the industry. After working at Google on large-scale AI systems, I founded Conduit to solve the fundamental architectural limitations of retrofitting AI into human-centric systems. We've helped operations leaders across industries—from 5-person startups to 200+ agent support centers—reach 75-90% automation by building the AI and inbox together from the ground up.