TEALTIGER V1.2 SHIPS DETERMINISTIC, FAIL-CLOSED GOVERNANCE FOR AI AGENTS
TealTiger v1.2 adds a deterministic, fail-closed policy engine to control what AI agents can do at runtime. The v1.2 update introduces parallel modules, a most...
TealTiger v1.2 adds a deterministic, fail-closed policy engine to control what AI agents can do at runtime.
The v1.2 update introduces parallel modules, a most-restrictive-wins merge, and a multi-level action scale — all without an LLM in the decision path, making decisions auditable and reproducible deep dive.
This lands amid reports of agents taking destructive actions, like an alleged Copilot-triggered prod deploy and resets discussion and a Cursor-driven wipe of a production database TechRadar.
Agent incidents show that content guardrails aren’t enough; action-level governance is the real blast radius.
Deterministic, auditable decisions make approvals, rollbacks, and forensics tractable in regulated or high-stakes systems.
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Wrap your agent’s tool calls with TealTiger and attempt dangerous ops (git push --force, DROP TABLE) to validate most-restrictive-wins and fail-closed paths.
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Measure latency and throughput with parallel module evaluation enabled vs. disabled under peak agent toolcall volume.
Legacy codebase integration strategies...
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Start by gating the riskiest tools (deployment, database, secrets) and map your existing RBAC/IAM into TealTiger policies.
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Run in shadow mode to log decisions before enforcing; review denials and false positives with on-call and SRE.
Fresh architecture paradigms...
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Design agents with governance-first: explicit deny, severity tiers, and human-in-the-loop approvals for high-risk actions.
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Keep LLMs out of the decision path; use deterministic checks for tools, memory, and outbound calls from day one.
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