Agent Smith — AI-powered pipeline execution.

Fetch the ticket

Agent Smith reads the issue from GitHub, GitLab, Azure DevOps, or Jira. Title, description, labels — everything becomes context.

Triage selects specialists

Based on what the bug touches — database, auth, API — Triage picks roles like backend-dev and tester. No hardcoding.

Roles plan until agreement

Each role can AGREE, OBJECT with an alternative, or SUGGEST an improvement. Rounds continue until consensus or human approval.

Agentic execution

The agent has file tools, bash, and your coding principles. It reads, writes, and iterates. Tests run automatically after every change.

PR opened. Ticket closed.

Code committed, PR created, ticket status updated. Token usage and dollar cost tracked in the result file.

Load the OpenAPI spec

Reads your Swagger/OpenAPI JSON or YAML. Maps every endpoint, parameter, auth scheme, and response type.

Spawn scanners

Runs Nuclei and Spectral against the live target. Nuclei probes for vulnerabilities, Spectral checks the schema for design issues.

AI panel reviews findings

Four specialists — vuln-analyst, design-auditor, auth-tester, and FP filter — review all findings against the OWASP API Top 10.

Convergence in rounds

Specialists AGREE or OBJECT. The design-auditor might challenge a vuln-analyst finding. Rounds continue until the panel aligns.

Findings delivered.

Filtered results in Markdown and console output. OWASP-mapped, severity-rated, with remediation guidance. Cost tracked.

Acquire the document

Upload a contract, NDA, or terms of service as PDF. Agent Smith extracts the full text and clause structure.

Five legal specialists activate

Contract analyst, compliance checker, risk assessor, liability reviewer, and clause negotiator — each with their own lens.

Clause-by-clause analysis

Each specialist reviews every clause. Compliance checks GDPR and liability caps. Risk assessor flags unfavorable terms.

Negotiator suggests alternatives

For every high-risk finding, the clause negotiator drafts alternative language you can propose to the counterparty.

Analysis delivered.

Findings as Markdown with risk ratings, clause references, and suggested rewrites. Token usage and cost tracked.

Static analysis first

Three automated scanners run before any AI touches the code: 91 regex patterns across 6 categories, git history scan for leaked secrets in 500 commits, and dependency audit for known CVEs.

Compress and slice

Raw findings are grouped by category and compressed — 74% token reduction. Each specialist gets only the slice relevant to their expertise, not the full dump.

Triage selects from 9 specialists

Vuln analyst, auth reviewer, injection checker, secrets detector, config auditor, supply chain auditor, compliance checker, AI security reviewer, and the mandatory false-positive filter.

Specialists review in rounds

Each specialist analyzes the code and their finding slice. They can AGREE or OBJECT. Rounds continue until consensus or max rounds (default 3). The FP filter removes noise: confidence < 8, infrastructure issues, duplicates.

SARIF + Markdown delivered.

Findings exported as SARIF 2.1 for GitHub Security tab, Azure DevOps, or any SARIF viewer. Markdown report for humans. Console output color-coded by severity. All formats combinable.

Frame the question

State any architectural or strategic decision. "Should we migrate to microservices?" "Rewrite in Rust or stay with Go?"

Five personas loaded

Philosopher (big picture), Dreamer (opportunities), Realist (constraints), Devil's Advocate (risks), and Silencer (noise filter).

Personas debate in rounds

Each persona argues from their perspective. They AGREE, OBJECT, or SUGGEST. Expect 2–4 rounds of heated but structured debate.

Convergence check

After each round, the system checks for consensus. The Silencer removes repetitive or low-value arguments. Debate tightens.

Decision delivered.

A structured verdict with pro/con arguments, conditions, and a clear recommendation. Token usage and cost tracked.