Planclave: Durable Intent for AI Agents
Making intent durable before execution begins.
Planclave provides a structured planning layer that sits between human intent and AI execution. By defining scoping, constraints, and priorities beforehand, it reduces agent drift, improves alignment, and makes execution easier to review, repeat, and refine.
The AI Execution Problem
AI agents can generate code, documents, workflows, and prototypes faster than ever before. But without explicit planning boundaries, they drift.
AI execution is cheap, but bad execution is still extremely expensive. It results in wasted time, runaway technical debt, and fundamentally misaligned deliverables.
Core Planning Components
Goals
Defining clear, measurable outcomes that the system must achieve, keeping the execution team tightly aligned.
Non-Goals
Explicitly establishing what will NOT be built. This is the single most effective shield against scope creep.
Constraints
Setting explicit technical, temporal, and resource limits that restrict the AI’s solution space within safe boundaries.
Acceptance Criteria
Providing strict, programmatic and human metrics that must be met before a task is considered complete.
Blueprinting with CodeSpec
CodeSpec is the code-focused implementation of the Planclave philosophy. It translates abstract human desires into structured blueprints that AI coding agents can ingest.
By establishing a strict contract before code generation begins, CodeSpec increases agent success rates dramatically. It is the architectural blueprint stage for professional agentic software development.
Proof & Progress
Planclave and CodeSpec are being refined through real-world deployment across several of our ecosystem components:
Philosophy
Explore the deep reasoning behind our development paradigms in the Planning is the Sleeper Skill essay.
Interactive Games
Inspect polished browser-based games built entirely from single-sentence prompts using early CodeSpec workflows on our Games page.
Ensign Karl Integration
See how Planclave powers the internal planning, decision loops, and cognitive architecture inside Ensign Karl.
Interested in Planning-First AI?
I am looking for early feedback, builders, and collaborators who are tired of letting AI agents drift off course.