A landscaping company in Raleigh wanted to automate their quoting process. They'd been doing it by hand for twelve years — measuring properties from satellite images, calculating materials, estimating labor, and emailing a PDF. The whole process took about 90 minutes per quote.
They hired a developer who built them an AI agent in three weeks. It could generate a quote in under two minutes. In the demo, it looked incredible.
In production, it quoted a 2-acre commercial property at $340. It told a residential customer the job would take "approximately 4 to 7 business centuries." It sent a quote to the wrong email address because it pulled contact info from a test record that hadn't been cleaned up.
The AI worked. The system around it didn't.
This is the gap between "demo-ready" and "production-ready." And it's where most AI projects die.
Why 40% of AI Projects Fail
That number comes from industry surveys, and it's probably conservative. The pattern is always the same: the AI itself is fine. It's smart enough. The model does its job.
What fails is everything else. The data pipeline. The error handling. The user isolation. The monitoring. The stuff that isn't exciting, doesn't make for a good demo, and doesn't get mentioned in the sales pitch. (We cataloged the 7 deadliest failure modes if you want the horror stories.)
Production-ready means the system works when things go wrong — not just when things go right.
Here's the checklist.
1. Define Boundaries First
Before writing a single line of code, answer this question: what is the agent allowed to do, and what is it not allowed to do?
This sounds obvious. It isn't. Most agent projects start with "let's see what it can do" and bolt on restrictions later. That's backwards.
A quoting agent should be able to look up pricing data, calculate estimates, and draft a quote document. It should not be able to send the quote to the customer without human approval. It should not be able to access financial data it doesn't need. It should not be able to modify pricing tables.
Write down the boundaries before you build. Think of it as the job description for your AI — what's in scope, what's out of scope, and what requires a manager's approval.
2. Set Strict Contracts
What goes in, what comes out. Define it. Be specific.
If your agent takes a customer request and produces a quote, define the exact format of both. What fields does the request need? What does a valid quote look like? What happens when a required field is missing?
Agents that accept anything and return anything are impossible to test and impossible to trust. Strict input and output formats make everything downstream — testing, monitoring, debugging — dramatically easier.
3. Lock Down Security
What can the agent read? What can it write? What can it execute?
The principle is simple: minimum access. If the agent needs to read customer records to do its job, it should be able to read customer records. It should not be able to read employee records, financial records, or anything else it doesn't need.
This is true for every tool the agent has access to. Database queries should be scoped. File access should be restricted. API calls should be limited to the specific endpoints the agent needs.
Think of it like giving a new employee a keycard. They get access to the rooms they need for their job. Not every room in the building.
4. Design the Context Architecture
This is where most projects go wrong without realizing it. The agent needs information to do its job — business rules, customer data, product catalogs, pricing tables. How that information gets to the agent matters more than which model you use.
Keep context layered and compact. Not everything needs to be in the prompt every time. A quoting agent needs pricing data for the current request, not the full price history for every product since 2019.
Context bloat — stuffing too much information into the AI — makes responses slower, more expensive, and less accurate. Design the information architecture before you build the agent. (We wrote an entire post on why context matters more than model selection — it's the single biggest factor.)
5. Govern Your Knowledge Base
If your agent uses a knowledge base — and most production agents do — that knowledge base is infrastructure. It needs governance.
Who controls what goes into the knowledge base? How does the information stay current? What happens when something changes — does the knowledge base update automatically, or does someone have to remember to update it manually?
A quoting agent that uses last year's pricing because nobody updated the knowledge base is worse than no agent at all. At least with no agent, someone would look up the current prices. If you're not sure how to organize this, start with how to package your business knowledge for AI.
6. Build Explicit Planning Steps
Good agents don't just react to inputs. They plan. They break a complex task into steps, check their work at each stage, and adjust their approach when something doesn't look right.
This needs to be built into the system, not left to chance. Ad hoc tool calling — "the agent will figure it out" — is not a plan. Explicit planning steps with checkpoints at each stage are.
For a quoting agent, the plan might look like: (1) Parse the customer request, (2) Validate the property details, (3) Look up current pricing, (4) Calculate the estimate, (5) Sanity-check the numbers, (6) Format the quote. Each step produces an output that the next step can verify.
7. Make State a First-Class Concern
Memory isn't an afterthought. It's architecture.
What does the agent need to remember between sessions? What happens if the process crashes in the middle of a task? Can it resume where it left off, or does it start over from scratch?
If starting over means re-running expensive operations, duplicating side effects, or losing context that took minutes to build — you have a state management problem.
Build state persistence into the system from day one. Not bolted on after the first crash.
8. Design for Failure
The agent will fail. The model will return garbage sometimes. The API will time out. The knowledge base will have a gap. A customer will ask something nobody anticipated.
The question isn't whether these things will happen. It's whether the system handles them gracefully or catastrophically.
Retry logic for transient failures. Fallback behaviors for unexpected inputs. Clear error messages instead of cryptic crashes. And above all — never let the agent send something to a customer that hasn't been validated.
A 95% success rate sounds good until you realize that means 1 in 20 customers gets a broken experience.
9. Track Everything
You cannot debug what you cannot see. Every agent action should be logged — what it decided, why it decided it, what tools it called, what results it got, and what it produced.
This isn't optional. It's not a nice-to-have for later. It's how you figure out why the agent quoted $340 for a 2-acre property.
Good logging means you can trace any output back to the exact sequence of decisions that produced it. When something goes wrong — and it will — you need that trail.
10. Build Drift Detection
Models change. Prompts get updated. Data changes. Over time, the agent's behavior will drift from what you intended.
Build mechanisms to catch that drift. Automated checks that flag when output patterns change significantly. Alerts when error rates tick up. Regular audits of agent behavior against expected baselines.
Without drift detection, you won't notice the gradual degradation until a customer complains — or worse, until they quietly stop using the system.
The Order Matters
These ten items aren't a buffet. They're a sequence.
Governance without contracts is guesswork. Observability without state management is incomplete. Planning without boundaries is dangerous.
Start at the top. Work down. Each step builds on the one before it.
What This Means for Your Business
If you're thinking about deploying an AI agent — for quoting, scheduling, customer service, reporting, or anything else — this checklist is the gap between a cool demo and a system your team can actually rely on.
You don't need to handle all ten items yourself. But you need to make sure whoever builds your system handles them. Ask your developer or vendor about each one. If they can't give you a clear answer, that's a red flag. (We wrote a full guide on how to evaluate AI vendors — these checklist items make great interview questions.)
The businesses that get real value from AI agents aren't the ones with the fanciest technology. They're the ones that treated deployment as seriously as they'd treat any other business-critical system.
Want to know where your business stands? Our AI Readiness Audit evaluates your infrastructure against this checklist and tells you exactly what needs to happen before you deploy.
Related Posts
Stay Connected
Follow us for practical insights on using technology to grow your business.