Most “AI agent” failures are not model failures.
They’re product failures.
The agent doesn’t ship a stable artifact. It doesn’t run on a reliable trigger. It can’t explain where it got its inputs. When it breaks, it breaks silently. Or worse: it breaks loudly and endlessly.
So people stop using it.
Reliability sounds like an engineering problem. For personal agents, it’s mostly a definition problem.
If you can’t answer “what does good look like?” you can’t make it reliable.
This post is a practical playbook for getting there without turning your life into a monitoring dashboard.
Define the artifact, then define “good”
A personal agent should produce a recurring artifact: a morning briefing, a pre-meeting packet, an inbox triage, a weekly bills watch.
The artifact is the product.
Reliability means: when the agent runs, you get that artifact, in the format you expect, with the content you need.
Not perfect content. Predictable content.
The minimum viable acceptance test
You don’t need a test suite. You need one question you can answer.
For each artifact, define a tiny acceptance test that fits in a sentence, and then write down one “bad” example so you can recognize regression.
For example, a morning briefing is good if you can answer “what are my commitments today and what’s the one thing that blocks me” in under 30 seconds. A bad morning briefing is a wall of items that makes you open your calendar anyway.
A pre-meeting packet is good if you ask a better question in the first five minutes of the meeting. A bad packet is one that restates the invite title and gives you nothing to decide.
An inbox triage is good if you can clear the day’s urgent messages without rereading threads. A bad triage is one that turns every message into a summary and still doesn’t tell you what to do.
A bills watcher is good if you catch one unwanted renewal before it renews. A bad watcher is one that lists every transaction and makes you feel guilty.
These are not fancy metrics. They’re behavioral checks.
And they have a nice property: they’re hard to game.
Make failures legible
If you want to keep using an agent, you need to trust its failures.
There are two rules that cover most cases:
First: missing output is a failure. If the trigger ran and there’s no artifact, the agent should send one alert and stop.
Second: missing input is not the same as “no changes.” If the agent couldn’t access email, it should not claim “no urgent emails.” It should say “email access failed.”
Legibility beats cleverness.
Log just enough to debug
People either log nothing or they log everything. Both are wrong.
You want lightweight logs that let you answer three questions later: what triggered this run, what inputs did it use, and what output did it produce.
For personal agents, a tiny JSON line per run is enough. You don’t need to store full email bodies. Store message IDs. Store counts. Store timestamps. Store the final artifact.
This matters because many “agent bugs” are actually configuration drift. Yesterday the agent read 20 items; today it read 200. Yesterday it used only calendar; today it also used a noisy notifications feed. Without a minimal log, you can’t see that change.
A good log is boring. It’s there so you can answer: did the agent behave differently, or did the world behave differently?
Drift is the real enemy
The most common reliability failure in personal agents is drift.
The agent starts helpful. Then the output slowly expands. More context, more caveats, more sections. It becomes “thorough.” You stop reading it.
Or the inputs drift. You add a new notification channel. You connect another account. Suddenly the agent is drowning.
The fix is not a better model. It’s a tighter contract.
Put hard caps in the spec: one screen. Three sections. One watch item. Max N inbox items.
If the agent can’t fit, it must compress.
The failure playbook you should copy
When a run fails, do not immediately tweak prompts.
Treat it like a production incident.
Start by asking: was this an input failure, a trigger failure, or an output failure?
If it’s input, remove the input. Go back to the last known-good minimal version.
If it’s trigger, make it boring. Scheduled beats event-driven when you’re trying to build a habit.
If it’s output, tighten the template. Remove a section. Add a cap.
Then rerun.
The goal is not to debug one run. The goal is to restore the contract.
A calm monitoring strategy (so you don’t get spammed)
Monitoring is where personal agents go to die.
If every small issue generates an alert, you’ll disable alerts.
Use a simple policy:
Alert only when the artifact was expected and missing.
Alert only once per failure mode.
Include one actionable clue: which input failed, or which step failed.
Then stop.
No retries forever. No looping. No “I will keep trying.”
A reliable agent knows when to wait for a human.
Closing
The quickest way to make an agent reliable is to treat it like a product you ship to your future self.
Define the artifact. Define “good.” Put caps in writing. Log just enough. Fail loudly when it matters, and quietly when it doesn’t.
That’s the boring stuff.
It’s also the stuff that makes it work.