Best Practices

Feature Overview

Best practices are not an additional feature, but a set of implementation methods that help you avoid detours. Their role is to turn “usable” into “stable, maintainable, and scalable” as quickly as possible.

Use Cases

Suitable for:

• Building core capabilities for the first time
• Preparing to move from pilot to production launch
• Reducing the risks of rework, hallucination, over-permission, and cost runaway

Recommended pre-launch check order

Step 1: Make Agent small first, not complete

The easiest mistake at the beginning is trying to make one Agent solve too many things at the same time. A more stable approach is:

• Solve only one core business goal at a time
• Stabilize basic conversations first
• Then gradually add knowledge, tool, or memory capabilities

Step 2: Make the knowledge base accurate first, not large

The key order for building a knowledge base should be:

1. Ensure material quality first
2. Then import samples for testing
3. Then gradually expand the material scale

If you only pursue document quantity at the beginning, later tuning costs will be very high.

Step 3: Run the workflow trunk first, then add complex branches

The most stable way to advance workflow is usually:

• Write the main process clearly first
• Run the shortest successful path first
• Then add conditional branches, loops, and exception handling

This is easier to succeed with than filling out the entire large process from the start.

Step 4: Establish governance boundaries first, then expand usage

After the platform starts being used by multiple people, you should prioritize establishing:

• Role and permission boundaries
• Team ownership
• API Key standards
• Model authorization strategies

If these boundaries are established too late, the platform will become increasingly difficult to govern later.

Step 5: Preserve test cases and regression habits

Whether it is Agent, knowledge base, or workflow, we recommend retaining:

• Fixed test questions
• Fixed input samples
• Regression checklists after changes

This makes it possible to know whether results have improved or degraded during later iterations.

Result Validation

When best practices are truly implemented, they usually show up as:

• Fewer instances of rework
• Faster fault localization
• Easier handoff to new members
• More platform capabilities without runaway complexity

FAQ

Why does the team become more chaotic even as it works harder on features?

Usually because there is no unified method. Everyone is adding features in parallel, but there is no stable advancement order or regression habit.

Why is “small first, big later” so important?

Because platform capabilities are highly coupled. If you build too large at the beginning, issues will appear across multiple layers at the same time, including model, knowledge, tool, and permissions.

Why should best practices be written as fixed rules?

If they remain only personal experience, they will stop working once the team grows. Only by forming fixed rules can platform capabilities continue to be handed off and reused.

Notes

• The focus of best practices is stability, not feature stacking
• Once a team forms an effective method, solidify it into internal standards as soon as possible
• After each retrospective, we recommend adding new lessons back into this method set