Make your codebase agent-ready.
Most teams are asking agents to work inside repos that hide the map, the rules, and the checks. CompoundCoders teaches the context systems, evaluation loops, executable docs, and repo structure that turn AI output into something you can actually review and ship.
// boss.fight.01
The agent is powerful.
The environment is the problem.
When agents fail inside real codebases, the failure is almost never one missing magic prompt. It's context, boundaries, docs, tests, or review discipline.
- Wrong-Path Implementation
- The agent solves the visible problem in the wrong layer because ownership boundaries are invisible.
- ▸ Fixes land in the nearest file, not the right module
- ▸ Architectural constraints are guessed, not read
- ▸ Review becomes archaeology
- Context Drift
- Long sessions fill with stale details, compressed assumptions, and forgotten constraints.
- ▸ The original task gets buried
- ▸ Earlier corrections disappear
- ▸ Reasoning from outdated context
- Weak Verification
- The code looks plausible, but nobody defined the checks that prove it matches the task.
- ▸ Tests run late, or not at all
- ▸ Acceptance criteria are vague
- ▸ Human review catches cheap misses
// system.upgrade
The agent-ready codebase system.
Stop treating AI coding as a prompt-by-prompt gamble. Learn the repo context, task scoping, evaluation, recovery, and documentation practices that let agents work inside real engineering constraints.
- Mindset
- Move from tool user to system designer with the Compound Coder thesis, reliability math, and the working loop that frames the rest of the course.
- Accurate Prompting
- Raise per-turn reliability with precise prompting, context-pollution discipline, reusable prompt anatomy, and a prompt system you can actually maintain.
- Context Engineering
- Architect what the model sees with persistent
AGENTS.mdcontext, just-in-time loading, lifecycle management, and tools like MCP. - Evaluation & Recovery
- Close the trust gap with inline checks, structured red team / blue team review, recovery runbooks, and a lessons loop that improves the next session.
- Agent-Oriented Repository Organisation
- Treat the repository itself as context: domain-first structure, executable docs, scoped work areas, boundary enforcement, and a 30-day implementation roadmap.
// quest.log
The 30-day implementation path.
Start with one repo. Four weeks. Then apply the same patterns to every codebase you work in.
- Week 01DAY 01-07
Audit & persistent context
Audit a real repository, identify context cliffs, and create a lean root AGENTS.md.
- Week 02DAY 08-14
Task context & executable docs
Create task brief patterns, improve README entry points, and turn documentation into usable context.
- Week 03DAY 15-21
Evaluation & recovery
Add quality gates, red team / blue team review patterns, and recovery moves for drift.
- Week 04DAY 22-30
Repo structure & continuous gain
Define boundaries agents must respect and capture lessons that improve the next session.
// the.workflow
See the workflow, not another toy demo.
Prepare the repo, assemble context, scope the task, verify the output, recover from drift, and capture what the session teaches you.
compound@retro: ~/workflow.mp4
v1.0
Player 01
Troels Frimodt Rønnow
Developer · Entrepreneur · Agentic Engineering Practitioner
Meet your guide to agent-ready engineering.
I've been wrestling with AI coding tools inside real systems for years. The leverage is real — but only when the codebase, context, docs, and checks are engineered for it.
I built compilers at Zilliqa, worked on quantum computing at Microsoft, and now work on a new blockchain called Rialo.
CompoundCoders is the system I wish I had earlier.
// player.check
Let's be honest about fit.
This won't work if...
- You're new to programming
- You need real engineering experience to benefit.
- You want blind autonomy
- This teaches controlled leverage.
- You expect AI to replace review
- AI output still has to earn trust.
Perfect if you're...
- An experienced engineer
- You already ship software.
- Getting inconsistent AI results
- You suspect context, tests, docs, and workflow are the issue.
- Responsible for a real codebase
- You want humans and agents to navigate it better.
// pre.sale
Lock in your founder's rate.
Pre-sale access is open now. Roughly 75% of the course is ready today, including the core templates, checklists, prompt packs, recovery playbooks, and 30-day roadmap.
Loading pricing...
// readme.faq
Questions I hear often.
- Q1. How will I find time for this?
- Start with one repository and the highest-leverage surfaces: AGENTS.md, README entry points, task briefs, and checks.
- Q2. Won't this be outdated?
- The course focuses on durable context, constraints, documentation, evaluation, recovery, and repo structure.
- Q3. What if AI tools create more bugs?
- That usually means the tool is operating without enough context, boundaries, or verification.
- Q4. Is this tied to one language or AI tool?
- No. It is about the workflow layer around AI coding.
// final.stage
Make every codebase agent-ready.
Start with one repository to learn the patterns. Then apply the same context, docs, checks, and recovery habits across every codebase you touch.