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Purpose
Mainstream programming languages are optimized for humans to prescribe procedures. Coding agents can generate those procedures quickly, but prose tickets, source code, policy, and evidence often remain separate—and a plausible implementation can quietly satisfy the visible tests while changing the intended meaning.
BHCP explores a different contract: declare outcomes, authority, limits, effects, preferences, and acceptance evidence in one typed semantic model. An implementation may choose how to achieve a goal, but it must not erase obligations, invent authority, or claim acceptance without the named evidence.
- People and organizations delegating consequential work to coding or execution agents.
- Tool builders who need deterministic intent artifacts, stable identities, and independently checkable evidence.
- Language and verification researchers testing whether semantic contracts improve correctness and claim calibration.
The project is trying to make four properties executable:
- Intent survives representation changes. Formatting, comments, labels, and safe custom layouts do not change semantic identity.
- Authority only narrows downward. Organization, team, repository, and user policy layers compose monotonically; a later layer cannot casually weaken an earlier one.
- Behavior grows outside the trusted core. Standard goal algebra is checked-in BHCP prelude source over a tiny behavior-neutral kernel.
- Completion is evidence-bound. “Implemented,” “verified,” and “accepted” are different states, and operational faults are not disguised as semantic rejection.
These are design commitments and active implementation targets—not proof that BHCP is already objectively superior to Python, TypeScript, Rust, or other general-purpose languages.
- Canonical and presentation-varied sources lower to deterministic, schema-valid artifacts with the expected identity invariants.
- Standard behaviors are self-hosted without new behavior-specific Rust primitives.
- Layered policy rejects every unauthorized weakening with stable diagnostics.
- Coding-agent experiments are frozen, reproducible, symmetrically judged, and honest about ties, overhead, variance, and failures.
- An agent can move an obligation from source to independently accepted evidence without a human reconstructing the contract after the run.
- A complete planner, distributed executor, mutable latch subsystem, or unrestricted agent backend.
- Arbitrary grammar plugins, macros, parser callbacks, or profiles that can change core meaning.
- Treating hidden tests as semantic authority or using a skill as a second language specification.
- Claiming broad productivity or correctness advantage from a small number of intentionally narrow pilots.
Read the vision for the long-term product direction and SEMANTICS for normative v0 behavior.