The Context Development Lifecycle described four stages: generate, evaluate, distribute, observe. That was the what. The flywheel is the why.
Better context produces better agent output. Better agent output generates better signals. Better signals produce better context. Each cycle compounds. By the tenth iteration, the team that invested in context is operating at a fundamentally different level than the team that kept tweaking prompts.
I wrote about this more fully on tessl.io. Here’s what I think matters most.
Four returns on one investment
When a senior engineer encodes their domain knowledge into tested, versioned context, four things happen at once:
- Agent quality improves. The agent handles edge cases it previously missed. Not because the model got smarter, but because the context got sharper.
- The expert gets sharper. Articulating tacit knowledge forces you to examine it. Writing down “we never do X because of Y” often reveals that Y hasn’t been true for two years.
- Juniors learn faster. Context files become living documentation. A junior reading a well-crafted skill file absorbs patterns and reasoning that would otherwise take months of pairing.
- The org aligns. Shared terminology increases precision. When everyone’s agents use the same conventions, the codebase converges instead of diverging.
This is the flywheel. One investment, four returns, each feeding the next cycle.
The competitive moat nobody’s building
Models commoditize. Every quarter there’s a new frontier model that’s 20% better at benchmarks. Tools converge. Every IDE has an agent now.
What doesn’t commoditize: institutional context. The edge cases your team catalogued over five years. The domain reasoning that lives in your senior engineers’ heads. The customer needs mapped through a thousand support tickets.
Organizations invest heavily in agents, models, and infrastructure. The overlooked investment: the contextual knowledge that makes everything work.
The team that encodes this knowledge into tested, versioned, distributable context has a moat. Not because it’s hard to copy, but because it compounds. Starting a year later means being a year behind in cycles.
Context rots without ownership
This is where most teams will fail. They’ll create context once, distribute it, and move on. Six months later, the context is stale, the agents are hallucinating based on outdated guidance, and nobody notices until production breaks.
Context needs the same discipline as code:
- Maintenance. Regular staleness reviews. If a context file hasn’t been updated in three months, flag it.
- Enablement. CLI tooling, CI-integrated evaluations, contribution workflows that don’t require a PhD in prompt engineering.
- Governance. Quality gates. Only validated context gets distributed. The same reason you don’t deploy untested code.
The self-tuning context work points to where this goes next: agents that evaluate and refine their own context through automated feedback loops. But even that needs governance. An agent optimizing its own instructions without guardrails is just automated drift.
The DevOps echo
If this sounds familiar, it should. DevOps solved the same structural problem: dev and ops had misaligned incentives, so we aligned them. Infrastructure as code made ops knowledge versionable. CI/CD made feedback loops fast. Monitoring closed the loop.
The CDLC does the same thing for context. The flywheel is what CI/CD was for deployment: the mechanism that turns a good practice into a compounding advantage.
The question isn’t whether your team uses AI coding agents. It’s whether you’re investing in the context that makes them actually useful, and whether that investment compounds.
Read the full post: The Context Flywheel: Why the Best AI Coding Teams Will Win on Context on tessl.io.