Stop Debugging Code. Start Debugging Decisions.

The most expensive bugs aren't syntax errors. They're ambiguous requirements.

February 25, 2026 10 min read

Your agent just shipped a feature. The code compiles. Tests pass. No linting errors. Everything works exactly as written.

And it's completely wrong.

Not broken. Wrong. It solves the problem nobody has. It optimizes for the metric that doesn't matter. It implements the requirement that was never actually what the user needed.

You have a decision bug, not a code bug. And no amount of debugging the implementation will fix it.

The wrong kind of debugging

When an agent ships the wrong thing, teams default to debugging the code. They review the implementation, check the logic, add more tests, adjust the prompt. The code gets better. The outcome stays wrong.

The real issue is upstream. Somewhere between the customer pain and the shipped feature, a critical product decision was either:

• Never made
• Made but not captured
• Captured but not accessible
• Accessible but ambiguous

The agent did exactly what any competent executor would do: it made its best guess based on incomplete information. The bug is that it had to guess at all.

What decision bugs look like

Example 1: The billing update

A SaaS company asked their agent to "add annual billing." Simple, right? The agent shipped it. Customers could now select annual plans. The code worked perfectly.

But the product team had never decided:

• Should existing monthly customers see upgrade prompts?
• What discount should annual plans get?
• Should grandfathered pricing carry over?
• What happens to prepaid credits on plan switch?
• When do billing cycles align: immediately or at renewal?

The agent made calls on all of these. None matched what the business actually needed. The result? Three weeks of rework fixing "working" code because the product decisions came after implementation.

Example 2: The notification system

A team asked their agent to "reduce notification noise." The agent analyzed usage, found that users were getting 50+ notifications per day, and implemented aggressive batching. Notifications dropped to 5 per day.

User complaints went through the roof.

The agent had optimized for volume reduction because that's what the prompt said. But the product team had never articulated:

• Which notifications are time-sensitive vs. batchable?
• What's the acceptable delay for different user roles?
• Are we solving for noise or for missing important updates?
• What does "important" mean for this product?

Perfect execution of an ambiguous decision.

Example 3: The dashboard redesign

"Make the dashboard faster" sounds clear. The agent optimized queries, added caching, lazy-loaded components. Load time dropped from 3s to 800ms. Huge win.

Except users weren't complaining about load time. They were complaining they couldn't find the data they needed. "Faster" was shorthand for "more usable." The product team knew this. The agent didn't.

The implementation was perfect. The interpretation was wrong.

Why we keep debugging code

Code bugs are visible and measurable. You can point at a stack trace, a failed test, a broken UI element. There's a clear "before and after." Fixing code shows visible progress because the artifact changes.

Decision bugs are invisible. They show up as:

• User confusion
• Support tickets asking "why does it work this way?"
• Features that technically work but nobody uses
• Rework disguised as "iteration"
• Silent abandonment

Teams don't have tools for debugging decisions. So they debug what they can measure: the code.

This is expensive.

The hidden cost

When you ship perfect code that does the wrong thing:

Time cost: The initial implementation, plus the rework, plus the time spent explaining why it needs to change.

Velocity cost: Every decision bug creates context thrash. Engineers revisit closed work. PMs re-explain requirements. Agents regenerate code.

Trust cost: After a few rounds of "it works but it's wrong," teams lose confidence in the agent. They add more review steps, write longer prompts, second-guess everything. The agent becomes a fancy autocomplete instead of a force multiplier. Watch a team go through this cycle three times and you'll see the shift: excitement turns to skepticism, velocity drops, and engineers start writing code by hand again because "it's faster than explaining it."

Opportunity cost: While you're fixing decision bugs, the features you should be building aren't getting built.

In our experience with 50+ engineering teams, most measure code quality. Almost none measure decision quality. So they keep shipping working code that misses the point.

How to debug decisions

Decision bugs have a root cause analysis, just like code bugs. The difference is where you look.

1) Trace the decision back

When a feature misses the mark, don't start with the code. Start with the decision path:

• What was the original user problem?
• Who defined the solution?
• What constraints or tradeoffs were discussed?
• What got written down?
• What did the agent see?

Usually, the break happens between "discussed" and "written down" or between "written down" and "agent sees."

2) Find the ambiguity

Most decision bugs come from underspecified requirements. Look for:

• Words with multiple interpretations ("fast," "simple," "better")
• Unstated assumptions ("obviously we'd handle that case")
• Missing edge cases
• Unclear prioritization ("all of these are important")
• Competing goals not reconciled

If you can interpret the requirement two ways, the agent definitely can.

3) Check for missing decisions

Sometimes the decision literally wasn't made. Teams say "add authentication" but haven't decided:

• What auth model? (username/password, OAuth, SSO, magic link)
• For which user types?
• What's the password policy?
• How do password resets work?
• What happens on failed login attempts?

The agent has to fill in these blanks. It will default to the simplest implementation—which is rarely what your product needs.

4) Surface competing constraints

Decision bugs often hide in unresolved tradeoffs:

• "Make it fast but add more data" (pick one)
• "Keep it simple but handle all edge cases" (these conflict)
• "Match the design system but make it feel premium" (depends on what "premium" means)

When constraints compete and nobody makes the call, the agent makes it for you. Usually wrong.

What changes when you debug decisions

One of our customers runs a B2B security product. They kept hitting the same pattern: agent would ship a feature, product team would say "that's not what we meant," engineers would rewrite it.

They started debugging decisions instead of code.

Their process now:

Before writing any code, they force themselves to answer:

• What specific user problem does this solve?
• What's the success metric?
• What are we explicitly not doing?
• What constraints apply? (performance, security, compliance, UX)
• What tone/style should this have?
• What edge cases matter?

They keep a decision log. Every time a requirement is ambiguous, they note:

• The ambiguity
• The decision they made
• Why they made it
• Who approved it

This log is available to the agent.

The result: Their rework rate dropped by 60%. Not because their agent got smarter. Because they stopped asking it to guess.

A framework for decision quality

Treat product decisions like you treat code. Apply the same rigor.

Decisions should be:

Specific: "Make it fast" → "P95 load time under 1s" Testable: Can you tell if the decision was followed? Contextual: Includes the "why" not just the "what" Accessible: The agent can find and use it Versioned: You can see when decisions change and why

When decisions meet these criteria, you ship right the first time. When they don't, you rewrite working code.

The agent forces the issue

Before AI agents, decision bugs were hidden in human communication. An engineer would build something, show it to the PM, the PM would say "not quite," they'd iterate. The ambiguity got resolved through conversation.

Agents can't do that. They execute on what's written. This makes decision bugs visible and expensive.

This is useful. It forces product teams to be explicit about what they're building and why. The teams that figure this out first will ship faster than everyone else—not because they have better models, but because they've eliminated the rework cycle. Teams that resist clarity will keep rewriting working code while their competitors ship.

How Brief handles this

We built Brief because we kept hitting this problem ourselves. Our agent would ship technically correct features that missed the product intent. We'd spend more time fixing decision bugs than code bugs.

Think of Brief as a product context layer that sits between your decisions and your agents. It captures the "why" behind your product—then surfaces it exactly when your agent needs it.

Brief surfaces the product decisions that should guide the agent before it writes code:

• User problems and priorities from past discussions
• Constraints and tradeoffs already decided
• Tone and quality bar for this feature
• Edge cases that matter for your product
• Past decisions on similar features

The agent still writes the code. But it's writing toward a clear target instead of guessing.

Learn how Brief eliminates decision bugs →

Practical steps

1) Keep a decision log

Create a simple doc or tool that captures:

• What decision was made
• Why (the constraint or tradeoff)
• Who decided
• When it can be revisited

Make this accessible to your agents.

2) Before you prompt, ask:

• Have we actually decided what "success" looks like?
• Are there multiple ways to interpret this requirement?
• What constraints apply that aren't obvious from the code?
• What edge cases matter?
• What tone/quality bar applies?

3) When rework happens, root cause it

Don't just fix the code. Ask:

• Was the decision clear?
• Was it accessible?
• Did we make the decision at all?
• What would have prevented this?

Update your process, not just the code.

4) Measure decision quality

Track:

• Features that ship without rework
• Time from decision to done
• Rework rate and root cause
• Decision debt (features built on unclear requirements)

Optimize for decision clarity, not just code velocity.

The real leverage

Code velocity is baseline now. Every team has access to fast agents. The differentiator is decision clarity—because clear decisions can't be copied by switching to a better model.

Teams that articulate clear product decisions will ship faster and with less rework. Teams that stay fuzzy will keep debugging working code.

The bug isn't in your agent's output. It's in your product input.

A diagnostic

Look at your last three rework cycles. For each one, ask:

1. Was the code technically correct? 2. If yes, what product decision was unclear? 3. Was that decision made but not captured? 4. Or never made at all? 5. If made and captured, could the agent access it?

If most of your answers point to decision quality, not code quality, you know where to focus.

What this means for your workflow

Stop treating agents like junior engineers who need detailed implementation instructions. Start treating them like senior engineers who need clear product context.

Senior engineers don't need you to specify every function. They need to understand:

• What problem we're solving
• For whom
• With what constraints
• To what quality bar

Give your agent the same. Focus your energy on decision clarity—the implementation will follow.

The compounding effect

Every clear decision makes the next feature easier. Your agent builds a model of your product priorities, your quality bar, your constraints. It makes better guesses.

Every ambiguous decision creates debt. Your agent makes the wrong call. You fix it. But the correction doesn't propagate. The next similar feature hits the same problem.

Decision clarity compounds. Decision debt compounds faster.

Start here

Pick your next feature. Before you prompt the agent:

• Write down the product decision in one sentence
• List the constraints that matter
• Note what you're explicitly not doing
• Capture the "why"
• Make it accessible

Then prompt the agent twice: once with just the feature name, once with the full decision context. Compare the outputs. The gap between them shows you exactly what decision clarity buys you.

You're not debugging code anymore. You're debugging decisions. That's the real work.