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Automatically Enriches These Failure Modes
Works with
Trustabl automatically generates clear when_to_use and when_not_to_use rules along with rich input validation. This gives agents precise guidance and dramatically reduces incorrect tool calls.
Fewer failed tool calls, higher task completion rate, and more reliable agent behavior.
Better schemas, applicability rules, and retry policies mean agents make fewer invalid calls and unnecessary retries, directly lowering token usage and external API costs.
Measurable reduction in token usage and API costs through fewer retries and better tool selection.
Faster debugging, clearer agent behavior, and reliable visibility into tool performance across every workflow.
View full observability specification →
Trustabl scans your tool code and automatically generates schemas, documentation, error handling, and observability configurations to help turn tool implementations into production-ready agent infrastructure.
Less manual engineering, faster tool deployment, and more reliable agent integrations ready for production.
Trustabl generates security-relevant metadata, produces ready-to-use OpenShell policy fragments, and supports SLSA provenance for verifiable supply chain trust.
Stronger security controls, easier policy enforcement, and verifiable trust across the tool supply chain.
Trustabl outputs native schemas for MCP, OpenAI function calling, Claude, GitAgent, and LangChain, making it easy to deploy trusted tools across your entire agent stack.
Faster adoption, less schema rewriting, and consistent tool behavior across models, frameworks, and agent environments.
Trustabl ingests real runtime signals from OpenShell and observability platforms such as Langfuse and LangSmith to detect issues, identify optimization opportunities, and suggest or apply improvements automatically.
Tools that continuously improve from real usage, with fewer recurring failures and more reliable agent performance over time.
Least-privilege sandbox policies
Native integration (MCP, GitAgent)
Reduced load, smarter high risk policy
SLSA + Sigstore attestations
Better tool calling accuracy
Resilient execution and self-recovery
Superior prompting and docs
Trustabl automatically hardens AI agent tools for production by generating rich, reliable metadata, including schemas, validation rules, retry policies, observability, security policies, and supply-chain attestations. It turns fragile, demo-grade tools into production-ready ones in minutes.
Trustabl is built for AI engineers, platform teams, and security/compliance teams who are building or running agentic systems in production and want tools that are reliable, observable, and policy-compliant.
Most tools are fully hardened in under 60 seconds. You connect your GitHub repo, and Trustabl scans, enriches, and generates most of the metadata automatically.
No. Trustabl works on top of your existing tools. It analyzes your code, documentation, and behavior, then generates enriched metadata and optional policy files without modifying your source code.
Just connect your GitHub repository at trustabl.ai. You’ll get an instant Production Readiness Score and can review or apply hardening suggestions in minutes.
Trustabl combines static code analysis, LLM reasoning, and domain-specific rules to automatically generate and enrich metadata across 12+ categories, including input/output schemas, validation rules, retry policies, error handling, observability configs, OpenShell policies, and SLSA provenance. Most fields are 70–95% automated, with optional human review for business-specific rules.
It’s a composite score (0–100) that measures how production-ready a tool is across schema quality, resilience, observability, security, and supply-chain integrity. Higher scores mean fewer failures and lower operational risk.
It attacks the root causes of agent failure: bad parameters, missing validation, poor error handling, wrong tool selection, and lack of observability. By enriching tools with this metadata, agents make fewer mistakes and recover faster when issues occur.
Yes. Trustabl analyzes code, manifests, docstrings, and runtime behavior regardless of language. It works especially well with Python, TypeScript/JavaScript, and any tool that exposes clear interfaces.
No. Skills (SKILL.md) teach the agent how to perform a task or workflow. Agentic Tool Metadata (ATM) makes the tools themselves reliable, safe, and production-ready.
Skills focus on process. ATM focuses on resilience, validation, policy, observability, and supply-chain trust. The two are highly complementary, great skills need hardened tools underneath them.
Yes. OpenShell secures the runtime environment. Trustabl hardens the tools the agents call inside that environment.
ATM automatically generates least-privilege policies, egress rules, binary requirements, and sandbox compatibility metadata that OpenShell can consume directly. Together they deliver defense-in-depth: secure runtime + production-hardened tools.
Smarter models can describe tools better, but they cannot reliably harden them for production.
Trustabl adds critical production-grade elements models cannot consistently provide: structured validation rules, circuit breakers, policy enforcement, cryptographic attestations, least-privilege OpenShell policies, and SLSA supply-chain provenance.
Trustabl automatically detects PII fields, suggests redaction and encryption rules, and generates appropriate data_handling metadata. You stay in full control, we never store or process your actual customer data.
Yes. Trustabl generates audit-ready metadata, including structured logging schemas, data lineage, retention policies, and SLSA attestations that make compliance evidence much easier to produce.
Yes. All hardening runs in your environment or GitHub Actions. We generate cryptographic attestations (Sigstore) so you can verify that metadata hasn’t been tampered with. We never require access to your production secrets or customer data.
Prompt testing tools evaluate prompts. Trustabl hardens the actual tools agents call in production.
We add validation, retries, error handling, policy enforcement, observability, and supply-chain attestations, none of which prompt testing tools address.
We’re complementary. Trustabl can automatically generate OpenTelemetry (OTEL) tracing, structured logging, and metrics configurations that feed directly into LangSmith, Langfuse, or any observability platform. We also surface key aggregated metrics and production readiness insights ourselves.
They focus on securing the agent runtime and detecting threats. Trustabl focuses on hardening the tools agents use so they are production-safe, policy-compliant, and resilient by design. We prevent problems at the source rather than only detecting them at runtime.
We offer a free tier for individuals and small teams, plus paid plans (Builder and Pro) for teams that want advanced features, higher usage limits, SLSA L3 attestations, and enterprise support. Enterprise plans are available for larger organizations.
Yes. You can connect your GitHub repo and harden tools for free with no credit card required. Many teams start by hardening their most critical tools to see the immediate impact.
You can trigger Trustabl scans automatically using our GitHub Action today. We’re also releasing official CI/CD plugins soon that will let you run scans, apply fixes, and generate attestations directly in your pipelines (GitHub Actions, GitLab CI, Jenkins, etc.).
Yes — this is coming soon. Our upcoming Tier 3 capability will let you define internal policies, approval gates, compliance rules (GDPR, PCI, SOX, etc.), and custom business logic that Trustabl automatically applies to your tools during hardening.
We’re actively building company policy enforcement, deeper OpenShell integration, expanded CI/CD plugins, and advanced analytics on tool usage patterns across your agent fleet.
Yes. Trustabl is essentially a specialized linter for AI agents.
While traditional linters like ESLint or Ruff focus on code style, syntax, and general bugs, Trustabl analyzes your AI agents, tools, prompts, and SDK configurations for reliability, safety, and production readiness — flagging patterns that expose you to prompt injection, missing timeouts, tool misconfigurations, and guardrail gaps that standard linters miss.
Think of it as “ESLint for AI agents” — it runs in CI/CD, gives clear explanations and fix suggestions, and helps you ship safer, more robust agentic systems.
No credit card required. Connect GitHub in under a minute.
