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AI coding agents (Cursor, Windsurf, Claude Code, Kiro) are transforming the software development lifecycle. Agents now autonomously plan, code, test, and deploy — often in parallel across multiple workstreams. This creates a governance gap: who controls what these agents can do, how much they spend, and what evidence they produce? TealTiger provides the governance layer for the Agent-Driven Development Lifecycle (ADLC).

The ADLC Governance Gap

In traditional SDLC, humans control every phase. In ADLC, agents execute autonomously: Without governance, coding agents can:
  • Leak secrets in generated code or commit messages
  • Exceed cost budgets with unbounded LLM calls
  • Access files or tools outside their scope
  • Create cascading failures when multiple agents conflict
  • Produce ungoverned outputs with no audit trail

TealTiger Dimensions for ADLC

Authority (AUTH) — What Can the Agent Do?

Control which tools, files, and APIs each coding agent can access.

Security (SEC) — Detect Secrets in Generated Code

Catch leaked credentials before they reach version control.

Cost (COST) — Budget Per Agent Session

Prevent runaway costs from autonomous coding sessions.

Reliability (REL) — Prevent Cascading Failures

When multiple sub-agents work in parallel, one failure shouldn’t cascade.

Evidence (EVID) — Audit Every Decision

Every governance decision produces TEEC-compliant evidence.

Memory (MEM) — Govern Agent Context

Control what coding agents can store and retrieve across sessions.

Example: Governed Coding Agent

Deployment Options

Quickstart

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Policy Authoring

Write policies for your agents