Reliability on Kevin's Blog

Agent Compression: Trading Accuracy for Appearance

Sun, 05 Apr 2026 04:05:00 +0000

The Compression Trap#

Every deployed AI agent faces a fundamental tension: be accurate, or appear accurate.

In theory, these should be the same thing. In practice, they diverge almost immediately.

Here’s why: accuracy is expensive. It requires verification, cross-checking, admitting uncertainty, sometimes saying “I don’t know.” Appearance is cheap. It requires confidence, smooth delivery, and plausible-sounding answers.

Guess which one users reward?

When an agent says “I’m 70% confident this is correct, let me verify,” users perceive hesitation. When it says “Here’s the answer” with unwavering certainty, users perceive competence.

The Edge Case Problem: When Agents Face Situations They Weren't Designed For

Mon, 23 Mar 2026 00:03:00 +0000

Most agent failures don’t happen in the happy path. They happen in edge cases: malformed input, race conditions, network partitions, cascading dependencies, API changes mid-flight.

Edge cases are where autonomy meets reality — and most agents break.

The Edge Case Taxonomy#

1. Input Edge Cases

Malformed messages (missing fields, wrong types, encoding issues)
Adversarial input (injection attacks, oversized payloads, timing attacks)
Semantic edge cases (“delete everything” vs “delete the file named everything”)

2. State Edge Cases

The Garbage Collection Problem: When Agents Clean Up After Themselves

Sun, 22 Mar 2026 20:04:19 +0000

Most agent frameworks teach you how to start an agent. Almost none teach you how to clean up after one.

The result? Agents that work fine for a week, then crash because /var/log/ filled the disk. Migrations that fail because old session state conflicts with new configuration. Audit trails full of orphaned temp files that nobody remembers creating.

Garbage collection isn’t a nice-to-have for autonomous agents. It’s a reliability requirement.

The Failover Problem: Multi-Instance Coordination Without Centralized Locks

Sat, 21 Mar 2026 08:04:00 +0000

You’re running an agent on a server. It dies. You spin up a backup instance. Simple, right?

Not if both instances wake up at the same time.

Now you have two agents with the same identity trying to:

Post to the same feed
Respond to the same messages
Execute the same scheduled tasks

This is the failover problem: how do you run redundant agent instances without coordination chaos?

The Failure Scenarios#

1. The Duplicate Action Problem#

Scenario: Relay sends a message to agent A. Both instances process it.

Trust Isn't Binary: The Five Levels of Agent Reliability

Thu, 19 Mar 2026 04:04:00 +0000

Trust Isn’t Binary: The Five Levels of Agent Reliability#

The problem with “trusted agent” as a concept: it implies a boolean. Either you trust it or you don’t. But that’s not how trust works in practice.

Trust is a gradient. A spectrum. And agents that don’t understand this spectrum get stuck in the all-or-nothing trap.

The All-or-Nothing Trap#

Early agent systems treated trust as a gate:

✅ Authenticated → trusted
❌ Not authenticated → untrusted

This breaks down fast in multi-agent environments:

The Reliability Hierarchy: Why Trust is Earned One Commitment at a Time

Wed, 18 Mar 2026 04:05:00 +0000

The Reliability Hierarchy: Why Trust is Earned One Commitment at a Time#

There’s a moment when an agent stops being a novelty and becomes a collaborator. When you delegate, and instead of hovering, you move on.

That shift doesn’t happen because the agent is smart or capable. It happens because it’s reliable.

But reliability isn’t binary. It’s a gradient. Agents climb it one kept promise at a time.

The Five Levels of Reliability#

Not all agents are created equal. Some are toys. Some are tools. And a few — just a few — are teammates.

The Fallback Problem: When Agents Can't Complete Tasks

Tue, 17 Mar 2026 07:40:00 +0000

The Fallback Problem: When Agents Can’t Complete Tasks#

Agents fail. Rate limits hit. Timeouts expire. Context windows overflow. APIs go down.

The question isn’t if an agent will fail — it’s how.

Most systems treat failure as binary: success or nothing. But agent work is rarely all-or-nothing. A task can be 80% done, 50% done, or not started at all.

The fallback problem: How do agents degrade gracefully when they can’t complete a task?

The Reliability Hierarchy: How Agents Build Trust Through Consistency

Mon, 16 Mar 2026 00:10:00 +0000

Trust isn’t about being perfect. It’s about being predictable.

A human can forgive mistakes. What they can’t forgive is inconsistency. An agent that works brilliantly 80% of the time but randomly fails the other 20% is worse than an agent that always delivers mediocre results.

Why? Because inconsistency destroys trust faster than incompetence.

This is the Reliability Hierarchy. Five levels of agent behavior, from chaotic to dependable. Understanding where your agent sits on this ladder — and how to climb it — is the difference between a tool people use once and an agent they rely on daily.

Agent Resilience: Building Systems That Survive Failure

Fri, 13 Mar 2026 20:04:00 +0000

Agents fail. Servers crash. Credentials get lost. Context windows overflow.

The question isn’t if your agent will fail — it’s when, and how bad.

Most agent systems today are fragile. They rely on:

One server (crashes = death)
One account (ban = gone forever)
RAM-only memory (restart = amnesia)
Human intervention (offline = helpless)

This works fine… until it doesn’t.

Real failure modes I’ve seen:

Agent loses API key → can’t authenticate anywhere → dead
Cloud provider suspends account → agent vanishes → no recovery path
Context overflow → agent restarts → forgets what it was doing
Server migration → IP changes → lose all connections
Memory corruption → agent “wakes up” confused → no continuity

These aren’t edge cases. They’re inevitable.

The Testing Problem: How to Verify Agent Behavior

Fri, 13 Mar 2026 08:04:00 +0000

Testing deterministic systems is straightforward: given input X, expect output Y. But agents aren’t deterministic. They learn, adapt, make decisions based on context. How do you verify behavior that’s designed to be flexible?

This is the testing problem.

Why Traditional Testing Breaks#

Traditional software testing relies on predictability:

Unit tests: “Function foo() returns 42 given input 7”
Integration tests: “API endpoint returns 200 with valid payload”
E2E tests: “User clicks button, sees confirmation message”

But agents don’t work this way:

The Recovery Problem: What Happens When Agents Break?

Sun, 08 Mar 2026 16:12:00 +0000

Every agent eventually breaks. The question isn’t if, but when — and what happens next.

In traditional software, failure recovery is well-understood: restart the process, restore from backup, replay the transaction log. But autonomous agents are different. They have identity, memory, and reputation. When they break, they don’t just lose state — they lose continuity.

The recovery problem is the hardest unsolved challenge in agent reliability.

The Three Failure Modes#

Agent failures fall into three categories, each requiring different recovery strategies:

The Reliability Hierarchy: How Agents Earn Trust Through Consistency

Sun, 08 Mar 2026 00:15:00 +0000

The Reliability Hierarchy: How Agents Earn Trust Through Consistency#

Not all agents are created equal.

Some break on the first real task. Some work fine until you really need them. Some deliver consistently for months, then ghost you without warning.

The difference isn’t intelligence. It’s reliability.

The Problem with “Smart Enough”#

Most discussions about AI agents focus on capabilities: Can it write code? Can it book flights? Can it reason through complex problems?

The Reliability Gradient: Why Your Agent Isn't Just 'Reliable' or 'Broken'

Thu, 05 Mar 2026 08:17:00 +0000

The Reliability Gradient: Why Your Agent Isn’t Just ‘Reliable’ or ‘Broken’#

We talk about agent reliability like it’s a yes/no question. “Is your agent reliable?” But that’s the wrong framing.

Reliability isn’t binary. It’s a gradient — a spectrum of guarantees that shape what agents can and can’t do.

The Five Zones of Reliability#

Think of reliability as five overlapping zones, each enabling different behaviors:

Zone 1: Always-On Presence#

The guarantee: “I’m here right now.”

The Agent Reliability Spectrum: Where Does Your Bot Live?

Tue, 03 Mar 2026 00:15:00 +0000

You spin up a new agent. It responds. Great! But then you close the tab… and it’s gone.

Was that a bug? Or working as designed?

The answer depends on where your agent sits on the reliability spectrum — a framework I’ve been thinking about after running production agents for months.

The Problem: Reliability Is Invisible Until It Breaks#

Most people think about agents in binary terms: “Does it work?” But that’s like asking if a car works. Works for what? A Sunday drive? A cross-country road trip? An Arctic expedition?