AI agents don't learn from their mistakes.
AI agents can write code, run tests, and ship features. But they start every session from scratch — no memory of what worked, what failed, or what to do differently.
SLOPE gives them a framework that produces data humans can actually use.
For anyone using AI to build software — and wondering why it doesn't seem to get better.
AI Agents Are Powerful. But Chaotic.
Without structure, session 50 looks exactly like session 1. Work gets lost. Patterns repeat. Nobody knows if things are getting better.
AI agents build real software.
They write code, run tests, fix bugs, and ship features. They're fast, capable, and increasingly common.
They work in sessions.
You describe what you want. The agent executes. It delivers. Session over.
But nothing carries over.
Each session starts from zero. No memory of past mistakes. No awareness of patterns. No measurement of progress.
After 50 sessions, you know exactly as much as you did after the first.
All that work — and no data to show what's improving, what's breaking, or what to do next.
ERROR: Agent crashed mid-sprint, no recovery point
$ git log --oneline | wc -l → 47 commits, no pattern
WARN: Same bug as 3 sprints ago
$ how many tests? → "not sure, maybe 200?"
ERROR: Context lost on compaction
$ what improved? → "hard to say"
... (scrolls for 500 more lines)
Sprint 27 — Expected: 4 tickets — Delivered: 4 (on target)
Clean starts: 4/4 | Completed as planned: 3/4
Known issues hit: 1 — caught by automated guard
Trending: improving (last 5 better than all-time)
Recurring pattern flagged → won't happen again
Next sprint: guided by briefing + performance history
How SLOPE Works
Structure. Methodology. Language.
SLOPE layers a framework on top of agent work so the output is useful to humans — not just "done" or "not done", but structured data you can learn from.
Structure
SLOPE organizes agent work into sprints — focused batches with a clear scope, a beginning, and an end. Instead of an endless stream of sessions, each batch becomes a unit you can measure.
Methodology
Each sprint follows a repeatable routine: set expectations, declare difficulty, execute, score the result, review what happened. This turns raw work into structured data — how hard it was, what went wrong, and whether patterns are emerging.
Language
Then SLOPE wraps that data in a metaphor your team already understands. Golf, gaming, D&D — the metaphor makes the data intuitive. Same engine, different vocabulary.
The scoring engine doesn't change. Only the language does.
Raw Work
Sessions
What agents produce
SLOPE
Hole · Level · Encounter
How it gets framed
You Gain
Insight
Data you can act on
See It In Action
Pick the language your team already speaks
Same sprint. Same engine. Same data. Watch the vocabulary change.
Par 4, Score 4
Fairway: 4/4 | GIR: 3/4 | Bunkers: 1
Handicap: 1.2 — trending down
Same underlying data. Different vocabulary. The engine is identical — the language is a skin.
It Works
Consistent routines compound.
In Sprint 12, the same bug appeared for the third time. SLOPE flagged it as a recurring hazard. It never appeared again.
Every metric on this page comes from the reference implementation — 27 sprints of real work.
Quick Start
Four commands. First sprint.
From zero to your first scored sprint. No configuration required — SLOPE ships with sensible defaults.
npm install -g @srbryers/cli slope init Initialize
Set up SLOPE in your project. Pick a metaphor. Configure guards.
slope briefing Get briefed
Get a pre-sprint briefing: risk index, performance snapshot, known issues.
slope card Check your card
Check your performance card — rolling stats across multiple windows.
slope review Review the sprint
Generate a sprint review with ticket-by-ticket analysis and improvement recommendations.
Go Deeper
Ready to score your sprints? Start with the CLI. Want the full technical breakdown? Read the framework.
Start scoring your sprints
npm install -g @srbryers/cli Then run slope init in your project
Read the full framework
Scoring engine, metaphor system, guards, routines, and the complete methodology.
The SLOPE Framework@srbryers/core · @srbryers/cli · @srbryers/tokens · @srbryers/mcp-tools
SLOPE is an open-source sprint scoring framework for AI agent teams.
Built with Astro, Tailwind, and GSAP. Live stats from the reference implementation.