Eval-Driven Development: How to Ship an AI Agent Without Guessing
Write the eval before the prompt. The test suite you build first is the only thing that lets you change models next month without praying — and in 2026, you will change models.
4 min
The Evals Are the Product
Agents got trivial to build and impossible to trust. The repos worth starring now aren't frameworks — they're the eval and tracing layer that tells you whether the thing actually works.
6 min
Online vs Offline Evals for AI Agents: Why Production Traces Need a Different Scorer
Offline evals ask whether the agent matched a known answer. Online evals can't — there is no answer. Treating them as one pipeline with one metric is the mistake that lets agents pass every test and still fail in production.
4 min
How to Build an LLM Eval Dataset
The scoring framework is the commodity. The hard, valuable, un-buyable work is looking at your own outputs and distilling real failures into labeled cases — your eval set is a precipitate of error analysis, not a download.
4 min
Git for Your Corpus: LanceDB Branching Makes RAG Evals Reproducible
LanceDB 0.34.0 added table branches — writes on a branch don't touch main. The headline feature is substring search; the sleeper is that the hard part of RAG evals was never the metric. It was holding the corpus still.
4 min
How to Add LLM Evals to CI/CD Without Building a Flaky Gate
You wire your eval into GitHub Actions, gate the merge on it, and a week later it's red on a PR that changed nothing. The fix isn't a retry — it's admitting an eval is a measurement, not an assertion.
5 min
LLM-as-a-Judge: How to Build an Eval That Doesn't Quietly Lie to You
Using a model to grade your model feels like measurement. Until you learn what the judge is actually rewarding — verbosity, position, and its own prose — it's closer to a focus group of one.
5 min
Your LLM Judge Is Biased: Position, Verbosity, and Self-Preference — and Which Ones You Can Fix
An LLM judge flips up to a third of its verdicts when you swap the answer order, and scores its own writing 10–25% higher. Three biases corrupt your evals — and only one has a cheap fix.
5 min
How Reliable Is Your LLM Judge? That's Half the Question
Rerun the same eval and an LLM judge flips 1 in 7 of its verdicts — while its own scores show no real difference between the answers. Reliability and validity are two different axes, and the number most teams report can't see either one.
5 min
Agent-as-a-Judge vs LLM-as-a-Judge: Grading the Trajectory, Not Just the Answer
An LLM judge scores the final answer. For a multi-step agent, that signal is sparse, late, and easy to fool — a broken trajectory can still land on a right answer, and you'd never know.
5 min
Do AI Agents Self-Correct? Why Reflexion Works and 'Check Your Work' Backfires
Telling an agent to review its own reasoning usually makes it worse, not better — and the reason it fails is the same reason Reflexion succeeds. Both come down to one asymmetry: verifying is only easier than generating when the verifier knows something the generator doesn't.
5 min
How to Evaluate an AI Agent's Tool Use, Not Just Its Answer
There is rarely one correct path through a task, so grading an agent against a golden trajectory fails. Grade invariants over the path, and the final state, instead.
5 min
How to Evaluate an AI Coding Agent
Public leaderboards answer 'which model is smartest,' not 'will it fix my bugs' — the only test that predicts your outcome is a private eval built from your own repo.
7 min
How to Evaluate a Deep Research Agent: Report Quality vs. Citation Accuracy
A deep research agent hands you a long, confident, well-structured report. Grading it means measuring two different things at once — how good it reads, and whether a single sentence is actually supported.
5 min
How to Evaluate a Voice Agent: Why Text-Agent Metrics Miss the Real Failures
Transcription accuracy is table stakes. The failure surface that actually loses calls is conversational timing — turn-taking, barge-in, and an end-to-end latency budget you have to measure component by component.
6 min
Pass@k vs Pass^k: Measuring Whether an Agent Is Reliable, Not Just Capable
pass@k asks whether an agent can ever solve a task. pass^k asks whether it solves it every single time. For long-horizon agents those are different questions — and the gap is where production failures live.
5 min
Cost-Aware Agent Evaluation: Why Your Benchmark Needs a Dollar Axis
An agent leaderboard that ranks only on accuracy is secretly ranking on willingness to spend. Add the cost axis and the board's #1 is often not even on the frontier.
5 min
SWE-bench vs τ-bench vs GAIA: Which Agent Benchmark Actually Predicts Production
They look like a difficulty ladder. They're three orthogonal axes — and only one of them measures the thing that decides whether your agent survives contact with real users.
4 min
SWE-bench Pro vs SWE-bench Verified: Why Top Coding Agents Dropped From 70% to 23%
The same models that ace SWE-bench Verified collapse on its successor. The gap isn't difficulty — it's the size of an illusion, and the only durable fix turned out to be a software license.
5 min
Terminal-Bench vs SWE-bench: Why Patching Code and Operating a Shell Are Different Skills
SWE-bench hands an agent a broken test and a healthy repo. Terminal-Bench hands it a live machine and lets it break things. That's why a top SWE-bench score tells you almost nothing about the second number.
5 min
τ-bench vs τ²-bench: The Agent Benchmark That Scores Whether You Can Guide a Human
Most agent benchmarks hand the whole task to the model. τ-bench keeps the user in the loop, and τ²-bench gives the user their own hands — which is where frontier agents quietly fall apart.
5 min
BFCL v4 Explained: The Function-Calling Leaderboard Stopped Measuring Function Calling
Berkeley's benchmark made its name scoring whether a model emits the right JSON. Its v4 rewrite puts 70% of the weight on agentic and multi-turn tasks — a quiet admission that single-shot accuracy is solved and no longer predictive.
4 min
SWE-EVO vs SWE-bench: The Long-Horizon Test Coding Agents Fail
A new benchmark drops the same models from ~73% to ~25% — not by making the bugs harder, but by taking away the one thing SWE-bench always handed over: a map to the change.
4 min
SWE-Together vs SWE-bench: The Benchmark That Counts How Often You Corrected the Agent
A new multi-turn coding benchmark reconstructs 109 real user sessions and scores agents on a second axis SWE-bench never had: not just whether they finished, but how much you had to steer them there.
5 min
GAIA2: The Agent Benchmark Where the Clock Never Stops
Static benchmarks freeze the world while an agent thinks. Meta's GAIA2 lets time run — and the smartest model, GPT-5, turns out to be the one that misses deadlines.
4 min
The Benchmarks Are Theater Now
When every frontier model clusters within a tenth of a point on the same saturated tests, the leaderboard stops measuring quality and starts measuring marketing.
4 min
How to Monitor an AI Agent in Production
Your agent can be HTTP-200, fast, and cheap while being completely wrong. The metrics that keep a web app healthy are blind to the ways an agent actually fails.
5 min
The Trace Is the New Log
Agent observability didn't invent a standard. It surrendered to a boring one from 2019 — and in doing so quietly retired the log as the unit of truth.
5 min
Honeycomb's Canvas Agent Auto-Investigates the Incident Before You Open Your Laptop
Most observability tools show you a dashboard and wait. Honeycomb's Canvas Agent starts the investigation itself the moment an alert fires — gathering data, forming and testing hypotheses, and proposing a fix — then hands a human the trail. For a founder who is also the on-call engineer, that's the difference that matters.
3 min
OpenLLMetry vs OpenInference: OpenTelemetry for LLM Agents in 2026
Both libraries emit OpenTelemetry spans for your agent. They disagree on what to name the attributes — and that disagreement, not the instrumentation, is your real lock-in.
5 min
Langfuse vs LangSmith vs Arize Phoenix: Choosing LLM & Agent Observability in 2026
The real choice isn't which dashboard looks nicer — it's what unit of work you trace and who owns the trace data after the agent finishes.
5 min
Langfuse vs LangSmith vs Braintrust: LLM Observability and Evals Compared
Three platforms that look like competitors but optimize for different primary jobs, with lock-in profiles that diverge sharply once you read the fine print.
4 min
Braintrust vs Arize vs Opik: Choosing an LLM Eval Platform in 2026
The eval-tooling field just split into three camps and lost two players to acquisition in a single month. Pick on philosophy and independence, not the feature grid.
3 min
DeepEval vs Ragas vs Promptfoo: Choosing an LLM Eval Framework
Three popular eval frameworks that look interchangeable answer three different questions — pick the one that matches the question you actually have.
5 min
Prompt Management: Langfuse vs PromptLayer vs Agenta (and Why a Registry Isn't Enough)
A prompt registry lets you change prompts without a deploy. On its own, that just lets you change them faster — not better. The tools that compound tie every version to an eval.
4 min