Measure and improve your agentic SDLC
Tuneloop analyzes every coding-agent session and links it to outcomes — so you can benchmark models on your own sessions and route each task to the cheapest one that holds up.
for platform teams reimagining the SDLC with AI
Can you answer these?
- Which work actually needs a frontier model, and what can move to cheaper models without losing quality?
- Did switching models change what ships?
- What did it actually cost to ship a feature?
- Are there teams or repos where AI struggles?
Benchmark agents and models on your own data
New models and agents land every other week, and you don’t have the cycles to keep up.Tuneloop turns real tasks from your own sessions into benchmarks, replays them on any model, and grades each against what actually shipped — a leaderboard that reflects your environment, not someone else’s.
400 tasks mined from sessions212 pass validation31 in this dataset
Bend the spend with smart routing
Think routine codebase exploration can move to a cheaper model? Replay it, check the gap, and route it with tuneloop-auto, in the Tuneloop router or as a policy in your gateway. You get routing rules justified by offline benchmarks, and continuous monitoring of live outcomes.
Tiers · cheapest first
Rules · most specific match wins
- task_typecoding:exploreorwriting:email→ t1
- task_typecoding:implementandcomplexitysubstantialoropen-ended→ t3
- task_typecoding:implementorcoding:debugorcoding:testing→ t2
- task_typecoding:plan→ t3
Watch outcomes on live traffic
Every task, routed or not, is tracked against what it produced: cost, speed, rework, and defects. If outcomes slip on routed traffic, you’ll see it.
Cost per Shipped Artifact
$31.03
per merged PR
What does a shipped result cost?
Time to Ship
12.3 days
median, ticket creation to resolved
How fast do features ship?
Code Churn Rate
18%
of lines rewritten within 30 days
How often does AI-written code get reworked?
AI Defect Rate
6.8%
of AI-assisted artifacts with a defect
Are AI changes causing more bugs?
Trace the spend to what ships
Tokens and PR counts measure activity. Tuneloop links every session to the PRs, tickets, and features it produced, so spend lands on outcomes, by feature, team, and repo.
Converted spend 71%Unconverted spend 29%
Measured automatically from your systems of record — not surveys, not token graphs, not vibes.
How spend attribution worksCapture · Link · Replay · Route
Capture
Transcripts from every agent session — Claude Code, Cursor, Codex, OpenCode, Pi, your own harness. They never leave your infra.
Link
Every session tied to the outcomes it produced, and split into tasks tagged by type, domain, and complexity.
Replay
Tasks from your sessions and merged PRs re-run on candidate models — score vs. cost per task.
Route
Configs that clear your quality bar become routing rules — in our router or your gateway.
savings verified against outcomes
We’re building Tuneloop now, with a few engineering teams. If you’re wrestling with the questions above, start with a model-mix audit of your own sessions.
Where your spend goes, by task type, and what could move.