Service · 03
NOC automation, AI agents, intelligent orchestration.
Your run teams have been reading the same alerts for years, following the same runbooks, spending time on actions that are perfectly automatable. We design workflows that take over the noise — to give human attention back to the actual problems.
Run a workshop →We have designed and operated sophisticated NOC pipelines — from linear alert-analysis workflows up to parallel agent models with knowledge graph, SLA breach detection, and Splunk HEC indexing. Here are the grounds we cover.
Incident detection and remediation pipeline
Multi-version architecture integrating CA Spectrum (equipment alarm detection), CMDB, ServiceNow (ticket creation and correlation), AWS Bedrock (contextual analysis via Claude), and Splunk (indexing and historical search). The pipeline evolves from linear logic (alarm → ticket) toward a parallel agent model: one agent qualifies, one enriches (CMDB, history), one proposes a remediation, one verifies execution.
Outcomes: auto-remediation of repetitive incidents, real-time SLA breach detection, Splunk HEC indexing for post-mortem, and a knowledge graph that learns from past resolutions.
n8n workflows for alert analysis
For environments where a heavy SOAR platform makes no sense, we build robust n8n workflows. Concrete examples:
- Dynatrace alert analysis: Splunk Cloud query via HTTP Request (Bearer token), pass-through to Bedrock LLM for synthesis, HTML report email
- Zscaler maintenance window monitoring via RSS feed + Bedrock processing + M365 Outlook notification
- Daily Cisco Meraki inventory: organisations / networks / devices scraped via REST API with rate-limit handling
- Zscaler ZIA policy extraction (URL Filtering, Firewall Filtering) with ZIdentity OAuth2 auth
- Retrieval of FortiManager policies via JSON-RPC for a given ADOM and policy package
- Read-only Fortinet → Zscaler comparative audit, generating gap reports tagged MISSING / MODIFIED / PRESENT / ORPHAN / NATIVE_ZIA
Multi-step AI agents (Bedrock AgentCore)
We design agents that go beyond "ask an LLM": step decomposition, short-term and long-term memory, tooled access (MCP, custom functions), human validation on sensitive actions. Hosting on AWS Bedrock AgentCore Runtime with strict access control (IAM, resource policies, PrivateLink), SigV4 authentication for outbound calls, and centralised logging.
Splunk Cloud integration (native workaround)
The native Splunk node in n8n is broken for Splunk Cloud — we know how to work around it via HTTP Request with Bearer token, proper SID handling, polling until job completion, and result parsing. Reusable patterns for your teams.
Automated reporting & dashboards
HTML email reports on configurable cadence, Splunk Classic dashboards for pre/post-migration comparisons (FortiGate, Meraki, Zscaler), MTTD / MTTR / alert volume per category. No magic — just usable numbers.
Typical use cases
Where we create value.
- NOC drowning in alert noise — auto-qualification and grouping
- Recurring low-value ServiceNow tickets — pattern detection and auto-closure
- Ongoing migrations — automation of cross-system comparisons and gaps
- Manual monthly reports — extraction pipeline + automatic generation
- Long investigations — AI agents pre-chewing the analysis for L2 / L3
- Operational compliance — execution evidence, traceability, Splunk indexing
FAQ
Frequently asked.
Do we need n8n or Bedrock already in place?
Do your workflows run fully autonomously?
How do you secure data sent to the LLM?
What ROI to expect?
Got a use case in mind?
One-hour workshop to qualify feasibility, effort, and expected ROI. No commitment.
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