How to Build for Longevity When AI Keeps Changing

I don't think anyone knows exactly how to build for longevity with AI right now. We can make reasonable decisions about the technology available today, but the changes go beyond models getting better at tasks we already give them. The interfaces are changing, along with our expectations of what software should do and how much of the work we should coordinate ourselves.

That uncertainty has forced me to go back to the fundamentals of AI systems design and think more carefully about what to build when so much is possible. What outcome am I trying to achieve? What does the system need to know about the business? Which parts of the build would still be useful if a different agent could do the work next month?

What you will learn

How to identify the part of a business problem that deserves a custom build.

How to structure that build so changing the agent or interface requires less rework.

The thing you are building might change shape

Grok Bot gives agents access to a persistent cloud computer. Meta's Muse announcement describes an agent with its own virtual machine and browser that can continue working after you close the app. These products suggest a different starting point for some projects: the agent may already have an environment in which to do the work. Grok Bot 101, Introducing Muse.

Writing in xAI's guide, employee Matt Palmer describes converting a fitness app he had built into functions and reusable instructions a bot could use. In his account, he kept the functionality while accessing it through a conversation. Grok Bot 101.

An ecommerce tool could go through a similar change. A comparison you imagined as a dashboard might be useful as a table an agent prepares when asked. A dedicated application may still be worth building when several people need to review the same information, but establish that need before committing to the interface.

Find the gap before building the application

Imagine a merchant who regularly compares supplier quotes. The outcome is to identify an option that meets the required delivery date and quantity, with a cost comparison the buyer can check.

Start by testing an existing agent with a few representative quotes and the purchasing requirements, using material you are authorized to share. Ask it to prepare the comparison, link each figure to its source and identify missing information. Check the result against the original documents.

What fails will help define the build. If the agent lacks purchasing context, a reusable instruction file may be enough. If it cannot reach approved supplier records, you may need a connection to that data. If quotes use inconsistent units or cost calculations keep going wrong, a small calculation tool could address a more specific problem.

If the existing agent handles the task well enough, save the instructions and examples, and put the workflow into use. A separate application becomes easier to justify when the trial reveals a need for shared review, a comparison history or more volume than a conversation can comfortably handle.

Include the time someone spends checking and correcting results when judging whether the approach is useful. A quick first output does not tell you how much work the system actually saves.

Separate the parts with different reasons to change

The framework I consistently reuse for building durable workflows is WAT: Workflows, Agent and Tools. It separates deterministic operations from the agent's reasoning, so calculations and other defined operations can be tested and reused independently of the model. Nate Herk describes the approach in The Shift to Agentic AI Workflows.

For the supplier comparison, I would separate them like this:

The workflow defines what a usable comparison must contain, including quantities, delivery requirements, source references and unresolved costs. Keep these requirements in a document you can update and reuse.

The agent reads unfamiliar quotes, identifies ambiguous terms and decides what needs clarification. These tasks involve interpretation, so its conclusions need to remain traceable to the documents.

The tools perform operations such as converting units and calculating totals from validated inputs. A deterministic calculation follows defined rules for the same inputs, although the rules and inputs still need to be checked.

This gives you specific places to make changes: update the requirements when a purchasing policy changes, and correct the calculation tool when a conversion is wrong. When testing a better model, you can start with the same requirements and calculation tools, then adapt and check how the new agent uses them.

The first version could live in a folder containing the purchasing instructions, sample quotes and a Python script (a deterministic tool) that calculates costs. Give a coding agent access to that folder and permission to run the script, whose instructions specify the figures it accepts and the results it returns. Another agent with code execution could use the same script; an agent without that capability would need a different connection to it.

Keep the quote data and comparison results somewhere the business can retrieve and export, with the source documents attached or linked. Another agent should be able to use the information without having to reconstruct it from a long conversation.

Put control around consequential actions

Separating these parts also helps distinguish a useful constraint from an unnecessary instruction about how to work. Requiring the comparison to flag missing shipping costs protects the decision. Requiring the agent to read every document in a prescribed order needs a reason related to accuracy or another actual requirement.

For this example, give the system access to the documents it needs and let it prepare comparisons without repeated approval requests. Keep a human decision before committing to a purchase, and enforce purchasing permissions in the connected systems. A sentence in a prompt cannot provide that access control.

The reviewer needs to see the source figures, calculations and unresolved questions together, so they can inspect the result without supervising every intermediate step. In this case, the important approval is the purchase decision; stopping for permission to read each quote would interrupt the work without changing who can commit the money.

Try this before replacing your agent

Before changing the model or interface, save a small set of representative quotes and checked comparisons, including cases with missing costs or ambiguous delivery terms. Record what a satisfactory result must get right and what it should leave unresolved.

When a new agent becomes available, run those cases again. Check whether it preserves the source references, uses the calculation tool correctly and flags the same material gaps. Also compare the time needed for human review. Passing a few cases will not establish reliability in every situation, but it gives you evidence for a replacement decision.

The original build may still require changes, but you have kept the requirements, calculations and examples available for the next implementation, which reduces the amount you need to rediscover.

A prompt to set up your first WAT workflow

Paste this into a coding agent with access to your project folder and describe the task in brackets. The prompt adapts Nate Herk's WAT approach into instructions for setting up a working automation, including the connections and checks it needs.

Help me build this automation using WAT
(Workflows, Agent, Tools): [describe the task and desired result].

First, clarify anything missing about:
- What starts it: a request, schedule or event.
- The systems and information it needs to access.
- The result, where it should go and how to check it.
- Business rules, exceptions and decisions requiring my approval.

Use those answers to set up and connect:

workflows/main.md
Document the outcome, inputs, tools to call, expected outputs,
checks, approval points and what to do when a step fails.

tools/
Implement reusable code for data access, calculations,
validation and system actions. Define each tool's inputs,
outputs and errors. Keep interpretation and judgment with
the agent. Reuse working integrations where available.

tests/
Add representative inputs and expected results, including
missing data and failed connections. Make retries safe so
rerunning a failed step cannot duplicate external actions.

An agent instruction file this environment loads
Explain how to follow the workflow, call tools, handle
exceptions and respect approval boundaries. Keep business
rules in the workflow rather than duplicating them here.

README.md and configuration
Install the required dependencies. Document setup, the run
command, required credentials and where outputs and logs go.
Use secure credential storage and exclude secrets, private
data and logs from version control. Enforce access limits
through tool permissions, not just written instructions.

Connect the trigger and runtime the automation needs.
If hosting or scheduling requires my account or a paid
service, explain the choice and get authorization first.

Run the workflow with representative data and external writes
disabled, and check the result against the agreed requirements.
After I authorize the external actions, verify one real run
through the configured trigger and output destination.
Report what works, what failed and anything still needed.
Label dry runs and mocked connections clearly; do not report
them as verified live integrations.

Key takeaway

For your next build, write down the outcome and test whether an existing agent can achieve it before specifying an application. Use the failures to decide what needs custom work, and keep the checked examples so you can revisit that decision when the technology changes. I cannot know how long the first implementation will last, but I can make it easier to carry useful work into the next one.