Think & Co | We Design, Build, and Operate Human-Centered AI Products for Businesses

TL;DR

Building is easy; operating is hard. Platforms let you deploy AI agents in minutes, but data quality, cost control, and reliability require expertise most businesses don't have.
The four hidden operational realities: usable data and context, unpredictable costs, hallucinations and errors, and behavior alignment with your brand.
There's no "set it and forget it." Effective AI operations require continuous monitoring, governance, and optimization.
Before you deploy: answer five critical questions about data, costs, failure modes, ownership, and measurement.
This week: run a "pre-flight checklist" on any AI agent you're considering to avoid expensive operational surprises.

The promise vs. the reality

If you've looked at AI agent platforms recently, the pitch is seductive:

Build intelligent agents in seconds. No code required. Deploy workflows in minutes.

And it's true, sort of.

You can build an agent in minutes. You can wire it to your CRM, your email, your support tickets. The demo works. It feels like magic.

But here's what the platforms don't say:

Building an agent is the easy part. Keeping it running well, without burning money, making mistakes, or creating more work than it saves, is where most businesses hit a wall.

The gap between "agent built" and "agent delivering value" is filled with operational work that non-technical leaders are rarely prepared for:

Getting the right data in the right format at the right time.
Handling token costs that can spiral into thousands per month.
Preventing hallucinations and errors that damage trust or revenue.
Tuning behavior so the agent actually sounds and acts like your company.

Platforms optimize for speed-to-demo. But speed-to-demo is not the same as speed-to-value.

The four operational realities no platform shows you

When you move past the demo and into production, you immediately run into challenges that weren't part of the sales pitch.

1. Data & Context: Your Agent Is Only as Good as What It Knows

AI agents don't "just work" with your data. They need:

Clean, structured data that they can actually parse and use.
The right context at the right time, emails, CRM records, past conversations, product data.
Ongoing updates so the agent doesn't operate on stale or incomplete information.

Most businesses have data scattered across multiple systems, some digital, some not; some current, some outdated. Getting that data into a form an agent can use is real work.

And even when you do, there are limits. LLMs have context windows, they can only "remember" a certain amount of information at once. Go beyond that, and they start to "forget" critical details or mix up information.

This isn't a failure of the technology. It's a fundamental constraint that requires thoughtful design: What does the agent need to know? When? How do we get it there reliably?

McKinsey's research on AI implementation consistently shows that data quality and integration are among the top barriers to AI success, with 70% of organizations struggling with data issues.

2. Costs: When Your "Cheap" Agent Runs Thousands Per Month

AI agent platforms often market themselves as "affordable" or even "free to start." But once you're running in production, costs can get out of hand fast.

Every time your agent:

Processes a request,
Pulls in context from your CRM or knowledge base,
Generates a response,
Calls an external API,

…you're paying for tokens. And those token costs add up.

A "simple" customer support agent that processes 100 requests per day, each requiring 3,000 tokens of context and 500 tokens of response, can easily cost $3,000–$5,000 per month at current API pricing, especially if you're using more capable (and more expensive) models.

And here's the kicker: most platforms don't provide built-in cost controls. You find out you've overspent when you get the bill.

For SMBs, this can turn a promising AI initiative into a budget problem.

3. Hallucinations & Reliability: When Your Agent Makes Things Up

LLMs hallucinate. It's not a bug; it's a core characteristic of the technology.

An agent might:

Invent a policy that doesn't exist.
Cite a document it never read.
Confidently give wrong information.
Misunderstand a customer request and take the wrong action.

This creates real business risk, especially when the agent has the power to:

Respond to customers,
Update your CRM,
Send emails or messages,
Make recommendations that affect revenue.

Research from arXiv and MIT on LLM reliability consistently finds that even state-of-the-art models make errors, and they make them confidently. The models don't "know" when they're wrong.

This means you need:

Validation layers to check outputs before they go out.
Human checkpoints at critical decision points.
Guardrails that prevent the agent from taking actions outside defined boundaries.

These aren't "nice to haves." They're operational necessities if you want your agent to be reliable.

4. Behavior & Brand Alignment: Making It Sound Like You

Even if your agent has the right data, controlled costs, and reliability checks, there's still the question:

Does this agent actually sound and behave like your company?

Out of the box, most agents are generic. They use safe, bland language. They don't understand your tone, your values, or the nuances of how you communicate with customers.

Getting an agent to:

Sound like your brand,
Know when to escalate vs. when to resolve,
Understand your specific policies and exceptions,

…requires tuning, refinement, and iteration. It's not a one-time setup; it's ongoing work.

And this work falls on someone, usually someone who's already overloaded.

The illusion of the "AI team in days"

The platforms create an illusion:

Launch your AI team in 48 hours. Scale instantly.

What they're really saying is: "You can deploy a demo in 48 hours."

Going from demo to production-ready, where the agent is:

Handling real customer requests without mistakes,
Operating within budget,
Aligned with your brand and policies,
Monitored and optimized for performance,

…takes expertise and time.

For most SMBs, this creates a critical gap:

Business leaders know what they want AI to do (faster responses, less manual work, better coverage).
But they don't have the operational expertise to get the agent from "cool demo" to "reliable business tool."

And because the platforms make it look easy, there's pressure to deploy fast, which often leads to:

Agents that make costly mistakes,
Runaway costs that weren't anticipated,
Projects that get abandoned after a few weeks because "AI didn't work."

The technology works. But "set it and forget it" doesn't.

What good AI operations actually looks like

If you want an AI agent to deliver real value, not just look good in a demo, you need to treat operations as seriously as development.

Here's what that looks like in practice:

The three pillars of sustainable AI operations

1. Measure & Monitor

Track costs in real-time (tokens used, API calls, daily/weekly spend).
Monitor errors and failures (where is the agent getting stuck or making mistakes?).
Measure business impact (response time, resolution rate, customer satisfaction, revenue effect).

If you can't measure it, you can't improve it, and you won't know if you're wasting money.

2. Govern & Validate

Define human checkpoints where an agent suggests but doesn't decide.
Set guardrails for what the agent can and can't do.
Create escalation rules so sensitive or high-risk requests go to a human.

This is the difference between an agent that saves time and one that creates liability.

3. Optimize & Iterate

Continuously review performance data and refine the agent.
Tune prompts and workflows based on real-world usage.
Adjust data sources and context as you learn what the agent actually needs.

AI operations isn't a "launch and done" project. It's an ongoing discipline.

The questions every business leader should ask before deploying

Before you hit "deploy" on any AI agent, answer these five questions:

What specific task is this agent doing, and what's the cost if it fails?
- If the agent makes a mistake, what's the business impact? (Lost deal? Angry customer? Compliance issue?)
What data sources does it need, and are they clean and accessible?
- Can you export a sample of the data the agent will use? Does it make sense to a human?
What's our monthly spend limit, and how will we monitor it?
- Set a ceiling. Track spending weekly. Know when you're approaching the limit.
Where do we need human review, and who's responsible for that?
- Identify the steps where a human must approve or override the agent.
How will we measure if this agent is actually delivering value?
- Define a baseline metric and a target. If you can't measure before/after, don't deploy.

If you can't answer these questions with specifics, you're not ready to deploy.

One action you can take this week

Here's a practical exercise you can do with your team in 30 minutes:

Run a "Pre-Flight Checklist" on Any AI Agent You're Considering

Pick one AI agent idea currently on your roadmap (customer support, lead response, internal Q&A, etc.).
Open a shared doc and answer the five questions above.
For each question, note:
- Green: We have this covered.
- Yellow: We need to figure this out before deploying.
- Red: We don't have this, and it's a blocker.
If you have more than two reds, pause deployment and address the operational gaps first.

This simple checklist will save you from expensive mistakes and help you separate "cool demo" from "valuable tool."

The real work starts after the demo

The AI platforms are right about one thing: building an agent is fast.

But building is only the beginning.

The real work, the work that determines whether your agent delivers value or just burns money, is in the operations:

Getting the right data in the right form.
Controlling costs before they spiral.
Preventing errors and hallucinations.
Aligning behavior with your brand and policies.
Measuring, monitoring, and continuously improving.

These aren't "technical details." They're business-critical decisions that every leader needs to own.

If you'd like help running a pre-flight check on an AI agent idea, or if you're already running agents and want to optimize their operations, we can help you build the operational foundation that makes AI work, not just in the demo, but in the real world.