Spec27 – Spec-driven validation for AI agents
AgentHi HN! We’re a team of ML validation specialists and we’ve been building /Spec27, a tool for testing whether AI agents still do their job safely and reliably as models, prompts, tools, and surrounding systems change.We started working on this because a lot of current LLM evaluation work seems a
Capabilities8 decomposed
spec-driven agent behavior validation
Medium confidenceValidates AI agent outputs against formal specifications defined in a domain-specific language, using constraint checking and assertion frameworks to ensure agents conform to expected behavior patterns. The system parses specifications into executable validation rules that are applied to agent responses, enabling deterministic verification of non-deterministic LLM outputs without requiring manual test case creation.
Uses formal specification language to declaratively define agent behavior constraints rather than imperative test suites, enabling specification reuse across multiple agents and automatic violation detection without code changes
Differs from traditional unit testing by validating against declarative specs rather than hardcoded assertions, and from prompt engineering guardrails by providing machine-readable compliance verification suitable for audit and governance
multi-agent specification consistency checking
Medium confidenceValidates consistency across multiple AI agents operating in the same system by checking that their outputs conform to shared specifications and don't contradict each other. Implements cross-agent constraint validation that detects conflicts when different agents produce incompatible results for the same logical domain.
Extends single-agent validation to multi-agent systems by defining inter-agent consistency constraints and detecting logical conflicts across agent outputs, enabling governance of distributed agent systems
Goes beyond individual agent testing by validating system-level consistency properties that emerge from multiple agents, which traditional testing frameworks cannot express without custom orchestration code
specification-based agent testing framework
Medium confidenceProvides a testing harness that uses formal specifications as the source of truth for test case generation and validation, automatically creating test scenarios from spec constraints and evaluating agent performance against specification compliance metrics. Implements property-based testing where specifications define invariants that must hold across all agent executions.
Derives test cases from formal specifications rather than manual test authoring, enabling automatic test generation and specification coverage metrics that traditional test frameworks cannot provide
Automates test case creation from specs (reducing manual effort vs pytest/Jest), and provides specification coverage metrics that reveal untested constraints unlike code coverage alone
real-time agent output constraint enforcement
Medium confidenceIntercepts agent outputs in real-time and applies specification constraints before responses reach users, enforcing hard constraints by rejecting or transforming non-compliant outputs. Implements a validation middleware that sits between agent execution and response delivery, with configurable fallback strategies (reject, transform, retry) when violations are detected.
Implements specification enforcement as a middleware layer with configurable fallback strategies (reject/transform/retry), rather than just validation reporting, enabling hard compliance guarantees in production
Moves beyond post-hoc validation to active enforcement with automatic remediation, providing stronger guarantees than logging violations or requiring manual review
specification versioning and evolution tracking
Medium confidenceManages specification versions and tracks how agent behavior changes as specifications evolve, enabling comparison of agent compliance across specification versions and detection of regression when specifications are updated. Implements a version control system for specifications with change tracking and impact analysis on agent validation results.
Treats specifications as versioned artifacts with change tracking and impact analysis, enabling specification evolution without losing compliance history or introducing regressions
Provides specification-level version control and regression detection that code-based testing frameworks cannot offer, enabling safe specification iteration
specification-driven agent debugging and diagnostics
Medium confidenceProvides diagnostic tools that use specifications to identify why agents fail validation, generating detailed explanations of constraint violations with execution traces and suggestions for remediation. Implements specification-aware debugging that maps agent outputs back to specification constraints and identifies which specification rules were violated and why.
Uses formal specifications as the basis for debugging, providing specification-aware diagnostics that map violations to specific constraints and suggest remediation based on specification structure
Provides specification-driven debugging that goes beyond generic error messages, enabling developers to understand violations in terms of business rules rather than low-level output properties
specification-based agent performance metrics and monitoring
Medium confidenceGenerates specification-aligned metrics that measure agent compliance, constraint satisfaction rates, and specification coverage in production, enabling monitoring dashboards that track agent health against specification requirements. Implements continuous compliance monitoring that aggregates validation results into metrics suitable for alerting and SLO tracking.
Derives monitoring metrics directly from formal specifications, enabling specification-aligned SLOs and compliance dashboards that traditional metrics frameworks cannot provide
Provides specification-specific metrics (constraint violation rates, coverage %) rather than generic performance metrics, enabling compliance-focused monitoring and alerting
specification-to-prompt optimization and synthesis
Medium confidenceAnalyzes specifications to identify gaps between specification requirements and agent prompt coverage, suggesting prompt improvements or automatically synthesizing prompt additions that address specification constraints. Implements specification-aware prompt engineering that uses formal constraints to guide prompt design and identify missing instructions.
Uses formal specifications to guide prompt engineering and automatically synthesize prompt additions, enabling specification-driven prompt optimization rather than manual trial-and-error
Provides specification-guided prompt improvement that goes beyond generic prompt optimization, using formal constraints to identify specific gaps and suggest targeted fixes
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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License: MIT
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Best For
- ✓teams building production AI agents that require deterministic compliance
- ✓enterprises deploying agents in regulated industries needing audit trails
- ✓developers iterating on agent prompts and wanting rapid validation feedback
- ✓multi-agent systems with shared knowledge domains
- ✓orchestrated agent workflows where downstream agents depend on upstream agent outputs
- ✓teams managing agent fleets with consistency requirements
- ✓teams adopting spec-driven development for AI agents
- ✓QA engineers validating agent behavior without deep ML knowledge
Known Limitations
- ⚠Specification complexity grows with agent task complexity — deeply nested conditional logic becomes difficult to express
- ⚠Validation is reactive (post-execution) rather than preventive — cannot guarantee spec compliance during generation
- ⚠Requires upfront investment in spec authoring; no automatic spec inference from examples
- ⚠Limited to validating outputs; cannot validate intermediate reasoning steps or chain-of-thought correctness
- ⚠Requires explicit specification of inter-agent contracts and consistency rules
- ⚠Performance scales with number of agents and specification complexity
Requirements
Input / Output
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