- Repository
- 8,420
- Documentation
- 3,180
- Decision history
- 2,960
Context control. Less in, more out.
Expensive models handle tasks a more deliberate path could solve. For AI engineering and platform teams who want to control what their agents actually need.
For CTOs, AI engineering, and developer platform teams.
The problem starts with context.
When agents load whole repositories, documents, and past sessions, cost and uncertainty grow together. This is token-maxing: expensive models for tasks a cheaper path could solve.
- 01
Too much gets loaded
Every task receives more context than it needs — and pays for it in tokens.
- 02
Behaviour stays unclear
Teams cannot explain what an agent read or why.
- 03
Models are misused
Expensive models solve work a smaller, more deliberate path could handle. That costs millions.
Context becomes a deliberate decision.
Thinkery selects task-relevant context before it reaches the model. Workflows become less expensive, clearer, and easier to control.
Discuss this workflowintent selects
- Auth module
- 1,280
- Security docs
- 420
- Prior decisions
- 310
What to measure.
Context baseline
What is loaded per task today?
Cost per successful task
What does a useful result really cost?
Relevance and quality
Is the right information used at the right moment?
Good questions before a pilot.
No. Thinkery starts with the workflow you already operate and creates a controllable layer around it.
Context volume, model and tool use, cost, quality, and the workflow’s concrete outcome.
Costs rise when agents load more context than a task needs — and expensive models process it. Thinkery addresses this at the source: before context reaches the model.
Start with one workflow.
We capture its current context, cost, and quality, then compare the same workflow with more deliberate context.