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How do you get useful work from a coding
agent, instead of just more code?

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Thorsten Ball's October twenty twenty-six
article offers fifteen principles,

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grounded in his own experience.

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The central shift is to delegate an
outcome, give the agent contact with the

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real system, and require evidence that it
succeeded.

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We'll turn each principle into a practical
habit.

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These are Ball's opinions, not universal
laws.

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The examples in this lesson are
illustrative.

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First, Ball argues for using the latest and
most capable models.

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His concern is hidden unreliability:

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you don't know which answers are wrong.

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A lower price per attempt may not mean
lower total cost if you spend the

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savings on retries and supervision.

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Apply this by comparing models on
representative work, including how much

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human correction each result needs.

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Second, stop assuming agent-generated code
inevitably becomes an

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unmaintainable mess. Ball is skeptical of
that prediction, given how models

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have improved. That's a personal judgment,
not proof that every generated

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change is good. Don't turn it into
permission to ignore defects.

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Ask whether the result works, whether you
understand its design, and whether

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you can change it later.

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Third, prompt the what, not the how.

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Instead of prescribing a cache, describe
the slow experience you want to

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improve. For example: halve response time
on this workload, preserve the

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returned data, and show before and after
measurements.

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You still specify constraints, such as no
public API changes.

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The agent chooses the implementation, not
the definition of success.

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Fourth, make the entire software
development lifecycle legible to the agent.

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Ball credits tool calls with letting agents
touch reality instead of

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inventing it. Let it run the application,
inspect logs, understand releases,

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and follow bugs through to their user
impact.

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A safe implementation separates visibility
from authority.

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Access to deployment information does not
automatically mean permission to

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deploy.

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Fifth, give the agent tools for that
lifecycle.

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Ball describes custom plugins for bug
reports, system health, customer email,

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and feature flags. The practical lesson is
to expose the operations the job

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actually requires, rather than describing a
system the agent cannot reach.

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For your own tools, scope permissions and
preserve approval gates for

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consequential actions. That's an
implementation safeguard, not a claim from

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the article.

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Sixth, evaluate agentic knowledge.

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Adding an instruction file or skill doesn't
establish that agents use it, or

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that it helps. Run blind agents on
comparable tasks, with and without the

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guidance. Keep the task and evaluation
criteria consistent.

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Compare observable outcomes, not whether an
agent says it read the document.

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Ball's suggestion is to experiment rather
than assume.

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Seventh, local development can become
awkward when several agents need to

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install dependencies and test applications
end to end.

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Ball prefers remote machines where each
agent can run what it needs without

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competing for the same local resources.

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In practice, isolation should include test
databases and services, not just

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separate source folders. Otherwise the
collisions simply move elsewhere.

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Eighth, don't iterate with the agent one
tiny step at a time.

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Let it build tools that make iteration
cheap.

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For design work, request twenty variations
and a page where you can compare

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and combine them, rather than twenty
separate conversations.

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For debugging, ask for four explanations of
the slowdown and experiments that

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test each one. Explore the problem as well
as the solutions.

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Ninth, ask for irrefutable proof.

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Ball calls this a major improvement in how
he works with agents.

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Request an end-to-end walkthrough, a demo
video, or before and after numbers

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from the same workload.

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Match the evidence to the claim.

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For a bug fix, show the original failure,
rerun the same scenario after the

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change, and check relevant cases that
should remain unchanged.

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Tenth, don't wait for your agents;

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let them wait on you.

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Ball recommends larger, more ambitious
tasks and asynchronous work.

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Give the agent enough context to finish a
meaningful unit, then do something

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else while it works.

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A good handoff includes the desired
outcome, constraints, expected proof, and

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the decisions that genuinely require your
input.

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Eleventh, use automation and schedules.

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Some important feedback arrives after the
initial task is finished.

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Ball's examples include checking logs in
two hours, notifying you when

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something is live, and performing a daily
check.

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Specify the trigger and what should be
reported.

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A monitoring request is not blanket
authorization to change production.

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Twelfth, stop manually managing context.

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Ball says compaction works well enough that
he doesn't organize work around

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the token count. His rule is one thread per
task, whether the conversation

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takes a few messages or thousands.

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That does not make memory infallible.

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Keep important constraints and decisions
explicit so the task can be checked

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and resumed.

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Thirteenth, threads become company memory.

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A patch shows what changed, but the
conversation can preserve why.

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Ball describes Amp threads connected to git
commits, making earlier reasoning

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available to other people and agents.

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Before reopening an old design question,
recover the previous investigation.

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Reusing that context can prevent duplicate
work across the team.

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Fourteenth, Ball advises against letting
agents write for humans.

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This is one of the article's deliberately
opinionated boundaries.

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He makes exceptions for mechanical writing,
including debugging instructions,

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incident reports, and some documentation.

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The useful distinction is between
transferring technical information and

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expressing your own judgment or
relationship with another person.

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Fifteenth, review code with high blast
radius.

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Ball focuses on a small number of central
files and database migrations,

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rather than every stylistic detail.

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He asks whether it works, whether he
understands the design, whether it was

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tested end to end, and whether he can
change it later.

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Apply the principle as risk-based review,
not an excuse to skip scrutiny of

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consequential changes. Proof and human
judgment serve different roles.

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Put the ideas together.

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Ask an agent to make checkout faster
without changing payment behavior.

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Give it a representative workload, an
isolated environment, and the tools it

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needs. Have it test competing explanations,
implement the improvement, and

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return comparable measurements plus an end-
to-end walkthrough.

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Let it work asynchronously.

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Review the payment-sensitive changes,
arrange a follow-up health check, and

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preserve the decision in the task thread.

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That's the operating model:

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outcomes, reality, and proof.

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This lesson adapts Thorsten Ball's article,
How I Work with Agents, published

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October twenty-fifth, twenty twenty-six.

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Read the original for his exact wording and
context.

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The examples and safety notes are additions
for teaching.

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They should not be mistaken for quotes from
the author.

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The narration and graphics in this video
are AI-generated.

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The takeaway: give agents a complete job,
and ask them to show you what

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actually happened.