Enterprise AI Agent Implementation for Ops Automation
Boolean & Beyond implemented a production AI agent system that triages, resolves, and escalates enterprise operations tickets with governance built in.
Overview
VertexOps runs support and internal operations for multiple business units with strict SLAs. We implemented an AI agent layer on top of their existing stack to automate repetitive workflows, reduce alert fatigue, and improve response quality without replacing core systems.
The Problem
Ops teams were handling thousands of repetitive tickets every week across Slack, email, and Jira. Manual triage created delays, escalations lacked context, and skilled engineers were spending too much time on low-value tasks.
Key Challenges
Fragmented Intake Channels
Requests entered through multiple channels with inconsistent formatting. Valuable context was spread across chat threads, knowledge bases, and historical incidents, making reliable triage difficult.
Inconsistent Escalation Quality
Escalations often missed logs, environment metadata, and ownership tags. Engineering teams had to ask follow-up questions before starting actual resolution work.
Strict SLA and Audit Requirements
VertexOps needed traceable actions, role-based approvals, and policy enforcement for every automated decision. Black-box automation was not acceptable in production.
Tooling Sprawl
Critical workflows depended on Jira, PagerDuty, Confluence, internal APIs, and customer records. Any agent architecture had to operate reliably across these systems.
How We Built It
Workflow Discovery & Safety Boundaries
Mapped top-volume intents, defined high-confidence automation zones, and documented approval boundaries. Designed fallback paths for low-confidence and high-risk decisions.
Agent Architecture & Integrations
Implemented an orchestrator agent with specialist tools for incident enrichment, runbook retrieval, ticket updates, and status communication. Added connectors for Jira, PagerDuty, and internal systems.
Guardrails, Evaluation, and Rollout
Added policy validation, output checks, and structured action logs. Ran shadow mode and controlled canary rollout with human review before enabling autonomous execution on selected workflows.
Optimization & Team Enablement
Fine-tuned prompts, routing, and tool retries based on production telemetry. Trained ops teams on intervention controls, confidence signals, and continuous improvement workflows.
Solution Highlights
Intent-to-Action Routing
Incoming requests are classified and routed to the right toolchain automatically. The agent identifies duplicate incidents, pulls prior resolutions, and attaches relevant context.
Context-Aware Escalations
When human escalation is needed, the system generates structured incident briefs with logs, impacted services, probable causes, and suggested next actions.
Governed Automation
Every autonomous action passes policy checks and role-based constraints. High-impact changes require explicit approval while low-risk tasks are executed automatically.
Ops Intelligence Dashboard
Real-time dashboards show automation rates, deflection quality, SLA performance, and recurring issue patterns to guide process optimization.
Technical Deep Dive
The implementation used a graph-based orchestration model where each node represented a deterministic tool call, retrieval action, or decision checkpoint. We implemented a hybrid retrieval layer combining runbook embeddings with metadata filters (service, severity, region) to keep responses precise and auditable. Tool calls were wrapped with retry policies, typed contracts, and circuit breakers to prevent cascading failures. Evaluation pipelines measured routing accuracy, action success rate, and escalation quality before each release. All actions were recorded with immutable trace IDs for operational and compliance review.
AI Capabilities
Intent Classification
Multi-channel request detection and workflow routing by urgency and domain
Runbook Retrieval
Context-aware retrieval from internal docs, incident history, and playbooks
Action Planning
Multi-step tool sequencing for safe and repeatable remediation workflows
Escalation Summarization
Generating high-signal engineering handoff packets with root-cause hypotheses
Policy Enforcement
Guardrail checks that enforce approvals, access controls, and execution scopes
Continuous Evaluation
Automated quality checks on routing, actions, and incident outcomes
Technology Stack
Agent Framework
AI Models
Backend
Integrations
Observability
Infrastructure
Results & Outcomes
68%
Ticket automation
Resolved end-to-end without manual intervention in approved workflows
4.2x
Faster triage
Average time to incident classification and ownership assignment
99.3%
SLA adherence
Improved consistency for high-volume operational queues
-37%
Ops handling cost
Reduced repetitive manual effort across support and platform teams
+52%
Escalation quality
Higher first-response completeness from engineering teams
24/7
Operational coverage
Continuous triage and response outside core support hours
“Boolean & Beyond gave us a true production agent system, not a demo bot. Our ops team now spends time on real problems instead of repetitive routing work.”
Head of Platform Operations
VertexOps
Services Used for the Client Product
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