Autonomous AI Agents
How a SaaS operations team secured agent-driven workflows that can execute tools, browse data, and trigger production changes.Challenge
An internal operations platform used autonomous agents to:- Triage incidents and open tickets
- Query logs and metrics
- Run controlled remediation scripts
- Summarize root causes for postmortems
Solution
KoreShield was inserted before any tool call or external API request. Each tool request was scanned, policy-checked, and either allowed, queued for approval, or blocked.Agent Workflow
The agent pipeline was split into deterministic stages to reduce ambiguity:
This kept each decision observable and enforced policy at every stage.
Guardrail Design
Tool Allowlist
Only approved tools were exposed to agents
Risk Tiers
Sensitive tools required human approval
Context Scopes
Tokens limited to a single incident or service
Rate Limits
Per-agent limits to prevent runaway loops
Approval Workflow
High-risk tools triggered an approval queue with a human operator:- Required Approvals
- Allowed with Warnings
- Auto-Blocked
Deploy, rollback, and shell execution operations
Monitoring and Auditing
Structured Logs
Structured Logs
Tool calls captured as JSON for SIEM ingestion
Per-Agent Limits
Per-Agent Limits
Daily caps for tool usage and tokens
Traceability
Traceability
Each response linked to the tool calls that produced it
Results
Zero Unsafe Calls
Eliminated unsafe tool calls in production workflows
Faster Response
Reduced incident response time with safe, automated triage
Full Auditability
Complete audit trail for tool usage and model outputs
Lessons Learned
- Agents are safest when tool access is narrow and explicit
- Approval queues should be fast and templated for on-call teams
- Policy tuning improves over time with real-world incident data
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