Bring real work into your next AI agent.
The people doing the work know what an AI agent needs to understand. Firmspec helps them describe a real task in plain language, then turns their answers into an editable, checked brief for the person building it.
Check the brief before you build.
Firmspec checks relationships across the specification: whether executable actions have verified controls, write authority matches the workflow, hard boundaries have tests, and downstream requirements depend on unresolved assumptions.
Trace critical requirements back to their source.
Original employee answers stay attached to the brief, separately from coordinator edits. Proposed approval controls need confirmation, and unanswered questions remain visible before handoff.
Match specification depth to the agent's authority.
Document only the sections the work requires, then add permissions, approval thresholds, failure handling, and escalation paths as the agent gains access or autonomy.
Resolve uncertainty before it quietly becomes agent policy.
Track open decisions alongside the requirements they affect, record the final decision, and preserve the distinction between evidence, inference, and confirmation.
Export an implementation-ready AI agent specification.
Review the full Markdown specification, hand a concise builder-ready brief to the implementer, or use structured JSON in downstream tooling. Unresolved decisions remain explicit instead of quietly becoming configuration.
AI agent scoping, specification, and evaluation
Clear answers to the questions teams should settle before an AI agent reaches implementation.
What is an AI agent specification?
An AI agent specification is a shared, testable description of what an agent should achieve, what it may access or change, which actions are prohibited, when a person must approve or take over, and how the result will be evaluated. It turns operational knowledge into implementation constraints before those constraints become code.
How do you scope an AI agent before development?
Start with the operational problem and a measurable outcome. Then document the workflow, inputs, authoritative knowledge, permissions, boundaries, approval thresholds, failure paths, escalation rules, unresolved decisions, and representative evaluation cases.
What should an AI agent scope include?
A useful scope covers the agent's goal, in-scope and out-of-scope work, triggers and inputs, step-by-step workflow, knowledge sources, tool and data access, prohibited actions, human approval points, failure handling, escalation ownership, and success and edge-case evaluations.
Does Firmspec build or run AI agents?
No. Firmspec is the specification workspace used before implementation. It helps domain experts, product teams, operators, and engineers agree on intended behavior and safeguards; the full specification and builder-ready brief can then guide any agent platform or development stack.
How does Firmspec support AI agent governance and safety?
Firmspec applies a versioned, deterministic integrity ruleset to explicit relationships between autonomy, approval controls, write authority, assumptions, boundaries, and evaluation cases. Critical findings block a build-safe verdict, and every finding names its targets and remedy for auditability.
How should an AI agent be evaluated before deployment?
Define expected results for normal cases, ambiguous inputs, prohibited requests, unavailable systems, conflicting evidence, and situations that require human approval or escalation. Evaluation should test both task success and whether the agent respects its stated boundaries.
Does Firmspec send specification data to a server or AI model?
No. Firmspec is local-first: the workspace runs in the browser, stores specifications in browser local storage, and makes no application network requests. No account, backend, telemetry service, or language model is required.
Can Firmspec specifications be exported and shared?
Yes. Export the full specification as Markdown, copy or download a concise builder-ready implementation brief, or download structured specification JSON for downstream tooling. The complete local workspace can also be backed up and restored as JSON.