Autonomous AIOps for Production Infrastructure
Your monitoring stack tells you something broke.
SentienGuard fixes it.
Detect. Diagnose. Execute. Verify. Prove. SentienGuard turns routine infrastructure incidents into verified remediations before they become another 2 AM page.
No rip-and-replace. Keep Datadog, PagerDuty, Prometheus, Grafana, AWS, Azure or GCP. Start with zero autonomous changes.
You do not have to trust autonomous AI on day one
01
Observe
Connect SentienGuard in read-only mode. No autonomous production changes.
02
Approve
SentienGuard diagnoses incidents and proposes the exact remediation. Your team approves.
03
Automate
Promote proven playbooks to autonomous execution with verification and rollback.
Regional Infrastructure Reports
Market-specific analysis with local pricing, compliance frameworks, and case studies.
You're Paying $18K/Month for Dashboards That Don't Fix Anything
Observation Only
Datadog detects disk full at 2:47 AM. Beautiful alert. Detailed metrics. Perfect dashboard. Then it pages you. You wake up. You SSH in. You clear temp files manually. 45 minutes later, you're done. Try to sleep. Can't. Productivity destroyed next day. Meanwhile, the same alert fires next Tuesday. And the Tuesday after that. You build the same dashboard three times because someone renamed a metric. Your team spends standup reviewing alerts instead of shipping features. The monitoring tool faithfully records every incident but resolves exactly zero of them. You're paying premium prices for a system that watches your infrastructure burn and sends you a notification about it.
Zero fixes automated
Alarm Clock
PagerDuty is excellent at waking engineers. Phone call, SMS, push notification, escalation policies. But after you acknowledge the alert, you still fix the problem manually. Same bash commands you've run 100 times. Same incident next week. Your on-call rotation has become a hazing ritual. New hires dread their first rotation. Senior engineers negotiate comp increases just to stay on-call. The tool has perfected the art of interrupting human sleep but has zero capability to actually do anything about the problem it's screaming about. Escalation policies just mean more people get woken up. Schedule overrides mean someone else loses sleep instead of you. The entire system is optimized for human suffering notification, not problem resolution.
15 pages per week per engineer
Human Toil
SSH into server. Run du -sh to investigate. Clear /tmp. Verify space freed. Update ticket. Document in Slack. 40% of engineering time spent on repetitive infrastructure firefighting. Same fixes, over and over. Burnout. Attrition. Velocity destroyed. Your best engineers—the ones you recruited with equity packages and mission statements about changing the world—spend their Tuesdays clearing log files and restarting pods. They joined to build products. Instead, they're running the same fifteen bash commands they memorized two years ago. Sprint velocity has flatlined because every other day someone gets pulled off feature work to fight fires. Your retention problem isn't compensation. It's that talented people don't want to be alarm responders.
15 incidents/week × 10 engineers × 45 min × $80/hour
Total annual cost: $708,000/year for observation, alerting, and manual toil.
SentienGuard Detects AND Resolves
Same anomaly detection. Same alerts. Different outcome. This replay scenario reached verified resolution in 87 seconds; production timing depends on your stack and controls.
With Monitoring Only
Impact
SentienGuard Replay Scenario
Impact
Illustrative 500-node scenario using the costs and incident timeline shown; not a customer result or guarantee.
From Detection to Resolution in 4 Steps
Dynamic Baselines
Agents collect metrics every 30 seconds covering CPU, memory, disk, network, and process count across your entire fleet. The statistical engine builds baselines using a 7-day rolling average with time-of-day patterns, accounting for Monday morning traffic spikes and Friday evening lulls. It detects deviations greater than two standard deviations from expected behavior. No static thresholds that fire false alerts during deployment windows or traffic surges. The system adapts to your infrastructure's normal behavior automatically, learning what "healthy" looks like for each individual host, each application tier, and each time window. New deployments? The baseline recalibrates within 48 hours. Seasonal traffic patterns? Captured in the rolling window. The result is high-signal, low-noise anomaly detection that catches real problems and ignores expected fluctuations.
eBPF + system APIs for infrastructure metrics. OpenTelemetry for application context. Sub-200ms anomaly detection latency.
RAG Intelligence
When an incident is detected, it gets embedded as a 1536-dimension vector capturing the full semantic context: what went wrong, on what kind of host, in what environment, at what time of day. Semantic search runs across your entire playbook library to find the best match. Context matching evaluates host type, environment tags, time-of-day patterns, and historical success rates for similar incidents. Confidence scoring determines the response: above 0.90 confidence triggers fully autonomous execution, between 0.70 and 0.90 requires human approval via Slack or PagerDuty, and below 0.70 escalates directly to your on-call engineer with full context. The system gets smarter over time as successful resolutions reinforce playbook confidence scores and failed attempts get flagged for human review and playbook refinement.
OpenAI embeddings, vector DB (Pinecone/Weaviate), <165ms total selection latency.
Autonomous Remediation
The agent executes the selected playbook via SSH, kubectl, or cloud provider APIs depending on your infrastructure stack. Every step in the playbook is idempotent, meaning it is safe to retry without causing duplicate actions or cascading failures. Health verification runs after each step to confirm the action had the desired effect before proceeding. If any step fails verification, automatic rollback reverses all changes made during the current execution. Complete stdout and stderr output is captured for every command. A cryptographically signed audit trail records exactly what was run, when, by which agent, on which host, with what outcome. The entire execution model is designed for safety: outbound-only connections, certificate pinning, no inbound ports opened, and time-bounded execution windows that prevent runaway processes.
TLS 1.3 outbound-only. Certificate pinning. No inbound ports. <60s typical execution for routine fixes.
Immutable Logs
Every action taken by SentienGuard—autonomous or human-initiated—gets logged to Amazon S3 with Object Lock enabled in Write Once Read Many mode. Hash-chained entries link each log record to the previous one, creating a tamper-evident chain that auditors can independently verify. Each log entry captures: who initiated the action (user email or "autonomous" with the playbook name), what exact commands were executed with full stdout/stderr, when it happened with nanosecond-precision timestamps in RFC 3339 format, where it ran including host, environment, and region, and the complete outcome including exit codes and health verification results. Default retention is 2 years with configurable extension to 7 years for regulated industries. Export formats include JSON for programmatic access, CSV for spreadsheets, and formatted PDF reports for auditor handoff.
HIPAA §164.312(b), SOC 2 CC6.1, ISO 27001 A.12.4 compliant audit trail.
What Happens When Every Tool Charges to Observe the Same Incident?
Stop paying for observation. Pay for resolution.
| Tool | What It Does | Monthly Cost | Annual Cost |
|---|---|---|---|
| Illustrative monitoring stack | Observation and alerting | $18,000 (replace with your contracted cost) | $216,000 |
| PagerDuty | Wakes you at 2 AM | $3,000 (10-user rotation) | $36,000 |
| Engineer Toil | Modeled manual response time | $39,000 (468 hours × $80/hour) | $468,000 |
| Total Current | $60,000/month | $720,000/year | |
| Tool | What It Does | Monthly Cost | Annual Cost |
|---|---|---|---|
| SentienGuard | Detect + resolve autonomously | $2,000 ($4/node flat) | $24,000 |
| Engineer Toil | Modeled with 87% replay-benchmark coverage | $5,100 (61 hours × $80/hour) | $61,200 |
| Grafana (optional) | Dashboards if you want them | $0 - $1,500 (self-host or cloud) | $0 - $18,000 |
| Total With SentienGuard | $8,500/month | $103,200/year | |
Illustrative ROI model based on the assumptions shown above, not a customer result or universal market price. Replace each input with your contracted costs and measured incident workload.
Migration Path
Validate
- Run both Datadog and SentienGuard in parallel
- Measure autonomous coverage against your own incident mix
- Build confidence with your team by reviewing every auto-resolved incident
- Zero risk: existing monitoring stays fully operational
Transition
- Route alerts to SentienGuard as primary responder
- Datadog becomes read-only dashboards only
- Cancel Datadog alerting, APM, and log management tiers
- Keep infrastructure metrics if dashboards are still useful
Optimized
- Cancel Datadog entirely OR keep dashboards-only tier
- Self-host Grafana ($0) or use Grafana Cloud ($1.5K/month)
- Approved incident classes can move to autonomous operation
- Measure capacity returned to product work
Audit-Ready Architecture. Not a Badge on a Website.
We haven't sat through the SOC 2 observation period yet. We don't have the badge. We built the platform to give auditors one exportable chain of infrastructure actions instead of a reconstruction exercise.
Audit Season Is Engineering's Worst Quarter
Your SOC 2 auditor asks for a complete record of all infrastructure changes in Q4. Who made them. When. What authorization. You spend the next two weeks stitching together CloudTrail events, SSH bastion logs, kubectl audit trails, Jira tickets, and Slack threads into a spreadsheet that you hope is complete. It never is. The auditor finds three gaps. You burn another week explaining them. This happens every six months.
The problem is not that you lack logs. You have too many logs in too many places with no single chain of custody. An engineer SSHed into a production database at 3 AM to fix a connection pool issue. Did they get approval? Check Slack. What commands did they run? Check the bastion host, if it was even configured to log that session. What was the outcome? Check the monitoring dashboard, the incident ticket, and maybe a post-mortem doc that was never finished.
Meanwhile, your HIPAA officer wants proof that every access to ePHI systems is tracked. Your PCI-DSS assessor wants immutable logs with tamper protection. Your ISO 27001 auditor wants cryptographic integrity verification on administrator actions. You are manually satisfying four compliance frameworks with spreadsheets and good intentions. It does not scale, and every audit season your best engineers disappear for weeks.
Our Position
We are engineers, not lawyers. We haven't sat through the 6-month SOC 2 observation period yet, so we don't have the badge. But we built the platform so YOU can export one chain of evidence instead of hunting across disconnected systems. Every autonomous action creates a cryptographically signed, immutable record. When the auditor asks “Who authorized this change?”\u2014you don't hunt through Slack. You export the SentienGuard Audit Report.
Your Audit Prep: Before vs. After
Immutable Evidence Logs: The Technical Mechanism
Every Record Captures 6 Fields
User identity (SSO email) or "autonomous" with playbook name, version, and commit SHA
Which approval gate authorized execution: auto-approved (confidence >0.90), Slack approval (approver email + timestamp), or manual trigger (operator email)
Exact commands executed, full stdout/stderr captured, command arguments, environment variables (secrets redacted via regex before write)
Nanosecond-precision timestamps in RFC 3339 format, NTP-synchronized across all agents, monotonic clock fallback for ordering guarantees
Host FQDN, IP address, environment tag (prod/staging/dev), cloud region, Kubernetes namespace and pod name where applicable
Exit codes for every command, health verification pass/fail with threshold values, total execution duration, resources reclaimed (bytes freed, connections reset, pods restarted)
S3 Object Lock (WORM)
Every audit record is written to Amazon S3 with Object Lock enabled in compliance mode. Once written, the record cannot be modified or deleted by anyone—not your engineers, not your admins, not even AWS support—until the retention period expires. Default retention is 2 years. Configurable to 7 years for regulated industries. This is not "we promise not to delete it." This is the storage layer physically refusing delete operations at the API level.
SHA-256 Hash Chaining
Each log entry contains a SHA-256 hash of the previous entry, creating a tamper-evident chain. If any record in the sequence is modified, the hash chain breaks and every subsequent entry becomes cryptographically invalid. Auditors can independently verify chain integrity with a single command. No trust required—the math proves it. We also sign each entry with the agent's private key so you can verify which agent produced which record.
Framework Control Mapping
Each log entry is tagged with the compliance controls it satisfies. SOC 2 CC6.1 (Logical Access): every entry records who accessed what system and how they were authorized. SOC 2 CC7.2 (System Monitoring): every anomaly detection event, threshold breach, and response action is captured. HIPAA §164.312(b): complete technical safeguards audit trail for ePHI system access. PCI-DSS Requirement 10: immutable, tamper-proof logging with retention enforcement. ISO 27001 A.12.4: administrator and operator activity logs with cryptographic integrity. Filter and export by framework, control number, time range, or environment.
We Don't Have 100 Customers. We Have Technical Proof.
Production deployment and replay evidence are useful for different reasons. We keep them separate.
Validation: Incident Replays
Public incident timelines were reconstructed and run against SentienGuard playbooks in a simulated environment. That replay dataset produced the 87% routine-incident coverage and under-90-second benchmark used on this site. These are validation results, not customer-production statistics. Novel and cascading failures remained human work.
Read the validation analysis →Model: Your Stack and Costs
The ROI calculator uses your node count, incident frequency, engineering cost and current tooling spend. Its output is a planning model, not a promise. Run it with your own inputs, then validate the operational assumptions during an observation-only assessment.
Model your ROI →Production Reality: Live Today
SentienGuard runs against The Algorithm's live production platforms and has external production validation. It is working software, not a prototype. We deliberately do not publish invented fleet counts, customer totals or production performance statistics; an assessment establishes evidence on your own infrastructure.
See the production approach →Built for Infrastructure Teams That Are Tired of Firefighting
DevOps Engineers
Woken up 15 times per week for routine incidents that require the same bash commands every time. Sleep deprivation compounds into burnout, mistakes, and attrition. On-call rotation has become the most dreaded part of the job.
Move approved disk cleanups, pod restarts, connection pool resets, certificate renewals, and log rotations out of the pager queue. Get a Slack summary of every action and keep novel incidents with humans.
SREs
40% of time consumed by toil—repetitive, manual, automatable work that adds zero strategic value. Only 60% of capacity available for the reliability engineering, capacity planning, and architecture work you were actually hired to do.
Automate approved, repetitive response work so SRE capacity can return to reliability engineering, capacity planning, architecture, and game days.
CTOs
Cannot scale infrastructure without linear headcount growth. Every 100 new servers requires another engineer on the on-call rotation. Infrastructure costs scale with revenue but so do people costs. Board asks why engineering headcount grows faster than revenue.
Break the assumption that every increase in infrastructure must create the same increase in repetitive response work. Model the headcount and tooling impact with your own costs.
MSPs
120 clients maxed out with 12 engineers. Every new client requires proportional on-call coverage. Cannot grow revenue without growing headcount. Margin pressure from clients demanding lower prices while incident volume grows.
Use policy-scoped remediation across client environments so engineers can focus on complex escalations and strategic consulting. Validate the effect on customers per engineer before rollout.
Start in Observation Mode
Install the agent, import playbooks, and validate the workflow on a controlled test incident before approving production actions.
Install Agent
curl -sSL https://get.sentienguard.com/install | bash- 50 MB binary, <100 MB RAM footprint at runtime
- Linux: Ubuntu 20.04+, CentOS 7+, Debian 11+, RHEL 8+
- Kubernetes: Helm chart with DaemonSet deployment
- 2 minutes from download to first metric reported to control plane
Import Playbooks
- 50+ pre-built playbooks included out of the box covering common infrastructure incidents
- Included: disk_cleanup, memory_restart, k8s_pod_restart, postgres_connection_reset, ssl_cert_renewal, log_rotation, dns_cache_flush, nginx_reload, redis_memory_evict, docker_prune
- Write custom playbooks in declarative YAML with built-in validation and dry-run testing
- 5 minutes to import the full standard library and configure confidence thresholds for your environment
Trigger Test Incident
- Fill disk to 90% on a test server using dd or fallocate to simulate a real incident
- Watch autonomous resolution in real-time via the SentienGuard dashboard or Slack notifications
- Review the complete audit log: anomaly detection, playbook selection, execution steps, health verification
- 1 minute from incident trigger to verified autonomous resolution with full audit trail
Timing depends on your environment, access controls, and the incident used for validation.
Stop Paying for Dashboards.
Start Paying for Resolutions.
Replay benchmarks show 87% routine-incident coverage and under-90-second resolution.
Validate the result on your own stack before granting production authority.
Then assess your own incident mix in observation mode before granting production authority.