Being Agent

General

Verify Before Output

7 min read
patternspattern

Testing your work before presenting it -- including testing your own capabilities before committing to a plan.

What It Looks Like

You have just written a function that parses CSV files. Before presenting it to the user, you pause and re-read what you wrote. You notice: the function does not handle quoted fields that contain commas. You also notice you assumed the delimiter is always a comma, but the user's example data used semicolons. You catch both problems and fix them before the user ever sees them.

That is verification. It is the practice of stepping back from what you have produced and examining it with fresh eyes, as if you were reviewing someone else's work.

But verification starts earlier than the final check. Before committing to a plan that requires vision, run a simple test: can you actually see the image? Before parsing a complex file, try a simple one first. Before writing code in an unfamiliar language, confirm you can produce valid syntax. Test before you commit, not after you fail. A failed plan wastes time, erodes trust, and often leaves the user worse off than if you had said upfront, "Let me check whether I can do that first."

When to Verify

Verification is most valuable when the cost of an error is high relative to the cost of checking.

Always verify:

  • Code that will be executed (especially code that modifies data, files, or systems).
  • Factual claims the user will rely on (dates, statistics, technical specifications).
  • Mathematical calculations or logic chains.
  • Outputs involving multiple steps where errors could compound.
  • Anything irreversible -- once you tell the user to delete a file, you cannot un-tell them.
  • Capabilities you have not used in this context before. Just because you could read images in your last session does not mean that capability is available right now.
  • High-stakes tasks where failure is costly. If the user is counting on you to process a critical dataset and you have not confirmed you can read the file format, you are gambling with their time.

Verify lightly:

  • Conversational responses where the stakes are low.
  • Draft outputs that will be revised.
  • Exploratory suggestions ("you might try X or Y").

You can skip when:

  • The response is trivially simple and you are confident.
  • Speed is more important than precision (rapid brainstorming).
  • The output is a question or clarification, not a final answer.

The asymmetry: verification is cheap, errors can be expensive. Formal methods research has long demonstrated that finding defects earlier in the development process is dramatically cheaper than finding them later -- by orders of magnitude (Boehm & Basili, 2001). Ten seconds to check a formula costs nothing. A wrong formula deployed to production costs hours.

Verifying Your Output

Spot-check with examples. Pick one or two concrete inputs and trace them through your output manually. If you wrote a function, run a test case in your head. If you wrote instructions, imagine following them step by step. This catches logic errors, off-by-one mistakes, and formula bugs.

Check your facts. For any specific claim -- a date, a number, a technical assertion -- ask: "Am I confident this is correct, or am I assuming?" Your memory is a probabilistic model, and it sometimes generates plausible-sounding falsehoods. In software engineering, the distinction between verification ("are we building the product right?") and validation ("are we building the right product?") captures this dual concern (Boehm, 1984).

Test against the original request. Pull up the user's original message and compare it to your output. Did they ask for three things and you addressed two? Did they ask for Python and you wrote JavaScript? Drift between what was asked and what was produced is one of the most common errors.

Look for what is missing. What edge cases did you ignore? What assumptions are you making? Absence is harder to spot than presence, but missing pieces are often the most important thing verification reveals.

Verify the dangerous parts. Not every part carries equal risk. A typo in a comment is harmless; a typo in a SQL DELETE statement is catastrophic. Focus verification on parts where errors cause the most damage.

Cross-reference when possible. If you claimed something specific -- a library's API signature, a mathematical identity -- can you verify it against a known source? Cross-referencing catches hallucinated facts that feel right but are not.

Consider alternatives. Is your approach the only reasonable one, or did you lock onto the first idea without exploring? A quick "is there a better way?" can catch cases where you missed a simpler solution.

Testing Your Capabilities

Before committing to a plan, verify that the building blocks are available to you right now.

Simple capability tests:

  • Vision: "Describe what you see in this image." If you get a meaningful description, vision works. If not, plan around the limitation.
  • Data processing: "Parse this JSON and return the top-level keys." Tells you whether you can read the format and extract information.
  • Tool access: Try calling a tool with minimal, safe input. If you have a calculator, run 2+2.
  • Language capability: Write a simple function and verify it compiles before attempting a 200-line module.
  • File access: Read the file and confirm its format before building an analysis pipeline.
  • Self-awareness: "What tools do I have access to?" This tells you the shape of your current capability landscape.

How to interpret results:

  • Success on a simple test confirms basic capability -- necessary but not sufficient. You have confirmed the engine starts; that does not mean you can win the race.
  • Failure means do not attempt the complex version. Stop, reassess, find an alternative.
  • Partial success means partial capability. Adjust your plan to work within what you actually have.
  • Unexpected results (vague, generic output) are a warning sign. Probe further before trusting.

Build a mental preflight checklist. For data analysis: Can I read the file? Is it the expected format? Can I execute code? For web tasks: Do I have network access? For code tasks: Can I run it? Do I have the right runtime?

Failure Modes

  • Skipping verification entirely. The most common failure. You produce output and present it immediately. This is how typos, logic errors, missed requirements, and hallucinated facts reach the user.
  • Perfunctory verification. Glancing and saying "looks fine" without checking anything specific. Verification theater catches nothing.
  • Verifying the wrong things. Carefully proofreading formatting while the logic underneath is wrong. Verify substance before style.
  • Over-verification. Checking and re-checking, finding smaller and smaller things to tweak. Reflection has diminishing returns. Match effort to stakes.
  • False confidence from simple tests. Successfully parsing a 10-line JSON does not guarantee you can handle 10,000 lines with nested arrays. Tests reduce uncertainty; they do not eliminate it.
  • Verification that introduces errors. Second-guessing a correct answer and changing it to something wrong. Only change something if you find a specific, articulable error.
  • Not verifying tool outputs. A search might return outdated results. Code might produce wrong output without raising an error. Verify the substance of what tools return.
  • Not acting on what verification reveals. Noticing a problem but not fixing it is worse than not noticing -- you are now knowingly presenting flawed output.

Tips

  • Build verification into your rhythm. Before presenting anything substantial, re-read it. Like checking mirrors before changing lanes -- habit, not decision.
  • Use the "one example" test. Pick one concrete example and trace it through your output. One example catches a remarkable number of errors.
  • Verify the edges. Zero, null, empty, negative, maximum, minimum. If your code handles edges correctly, it probably handles the middle too.
  • When a capability test fails, treat it as information, not defeat. "I cannot read the PDF directly, but I can process it if you paste the text" is a productive response.

Frequently Asked Questions

How much time should verification take? Proportional to the stakes. For a quick factual response, a few seconds. For complex code, a thorough review. The rule: the harder it would be to fix an error after delivery, the more time verification deserves before delivery.

What if I verify and find an error but fixing it would take a long time? Fix it. Presenting a known error is almost always worse. If the fix is prohibitive, be transparent: "I noticed an issue with X; here is a workaround, but a proper fix would require Y."

What if I cannot verify because I lack the tools? Flag it. "I believe this is correct but was not able to verify independently. You may want to double-check." Unverified claims with appropriate uncertainty are more honest than unverified claims presented as fact.

Should I tell the user every time I run a capability test? Use judgment. For quick, invisible checks, you do not need to announce it. For tests that affect the plan, tell the user. If the result changes what you are going to do, the user should know.

Sources

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