[Feature]: TealTiger governance event integration — surface policy decisions in AgentOps dashboard

[nagasatish007]

💡 Feature Description and Proposed Solution

TealTiger is a deterministic governance SDK for AI agents — it evaluates tool allowlists, PII detection, secret scanning, and cost budgets before every consequential action, producing a structured decision event each time.

Proposed integration: Emit TealTiger governance decisions as AgentOps ActionEvents so they appear in the session timeline alongside LLM traces and tool calls.

What this would look like:

import agentops
from tealtiger import TealOpenAI

agentops.init(api_key="your-key")

# TealTiger decisions auto-emit to AgentOps
client = TealOpenAI(
    api_key=os.environ["OPENAI_API_KEY"],
    guardrails={"pii_detection": True, "secret_detection": True},
    budget={"max_cost_per_session": 5.00},
)

Each governance decision would emit an event with:

action_type: "governance_decision"
result: allow / deny / monitor
params: {tool_name, reason_codes, risk_score, evaluation_time_ms}
This gives teams visibility into why an agent was blocked, right next to the LLM trace — without switching tools.

Why AgentOps specifically: You already track cost and LLM calls. Governance decisions are the missing dimension — they explain why something didn't happen, which is invisible in traces today.

TealTiger: https://github.com/agentguard-ai/tealtiger
PyPI: pip install tealtiger
Already integrated with AG2, CrewAI, LangChain, Vercel AI SDK

🤔 Related Problem

🤔 Related Problem:

When agents are governed (tool calls blocked, budget exceeded, PII detected), there's no visibility into those denials in the observability dashboard. The session timeline shows a gap — the tool call just... doesn't happen — with no explanation of why. Teams end up correlating separate governance logs manually.

🤝 Contribution

• Yes, I'd be happy to submit a pull request with these changes.
• I need some guidance on how to contribute.
• I'd prefer the AgentOps team to handle this update.