Your Engineers Are Too Good
to Be Babysitting Pipelines.
Agentic AI handles the ops toil — incident triage, test generation, release checks, anomaly response — so your team focuses on what actually moves the product forward. Built into your existing stack. No rip-and-replace.
Your DevOps is working.
That's exactly when this matters most.
The highest ROI from AI automation isn't when things are breaking. It's when the toil is quiet, predictable, and completely invisible — until you add up the engineer-hours.
Same incidents, same manual response, every time
Your on-call engineer gets paged at 2am for something that's happened 14 times before. They run the same five commands. The fix is the same. This is exactly what an agent should be doing — not your best engineer.
Test coverage is always behind the code
Every sprint, new code ships faster than tests can be written. Agentic AI can generate test scaffolding, suggest edge cases, and flag coverage gaps before the PR lands — not after QA finds it in staging.
Release checks are still a human checklist
Someone still has to walk through the pre-release checklist. Check the dashboards. Confirm dependencies. Send the go/no-go. Every time. Even at midnight on a Friday.
Your observability data isn't being acted on
You have metrics, logs, and traces. What you don't have is something that connects the dots across all three and takes action before your users notice. That gap is what agentic AI closes.
Six types of agents — deployed into
your actual environment.
Not a demo. Not a sandbox. We build, test, and hand over agents running in your real pipeline — connected to your real tools, trained on your real incident patterns.
Incident Triage Agent
When an alert fires, the agent reads your logs, checks related metrics, queries your runbook history, and posts a structured triage summary in Slack before your on-call engineer has even opened their laptop. Dramatically cuts mean-time-to-resolution.
Automated Test Generation Agent
Analyses new or changed code in PRs, identifies untested paths, and generates test scaffolding — unit tests, edge cases, integration hooks — as a PR comment or direct commit to a test branch. Coverage improves without adding to the sprint backlog.
Release Gate Agent
Runs the pre-release checklist automatically — dependency checks, config validation, environment health, rollback readiness — and produces a signed-off go/no-go summary. Eliminates the release-day scramble and makes the process auditable.
Pipeline Self-Healing Agent
Monitors your CI/CD pipeline for flaky tests, resource bottlenecks, and intermittent failures. When it detects a known failure pattern, it retries with adjusted parameters or flags it with context before it blocks the team.
Infrastructure Cost Optimisation Agent
Continuously monitors cloud resource usage, identifies waste from over-provisioned instances and idle resources, and surfaces actionable recommendations — or acts directly where you authorise it to. Most teams find 15–25% cloud cost savings within the first month.
Documentation and Runbook Agent
Watches your deployments, incidents, and pipeline changes and automatically updates runbooks, architecture docs, and change logs in your wiki. The documentation that always falls behind? This is what fixes it — without adding it to anyone's to-do list.
The agent your team needs
depends on what you ship.
Fintech & Banking
Automated compliance checks and release gate sign-offs before every production push. Audit trail generated without a single manual step.
Healthcare & MedTech
Agents that validate model versions, generate evidence for FDA submissions, and flag config drift in regulated environments before your QA team catches it.
SaaS Platforms
Incident triage agents that correlate multi-service alerts and reduce on-call pages by up to 60% by catching anomalies before users see them.
Embedded & IoT
OTA update agents that validate firmware build integrity, check device fleet readiness, and roll back automatically if anomalies are detected post-push.
Automotive
Agents that monitor telematics pipelines, flag data quality issues in real-time ingestion, and trigger alerts when sensor data patterns fall outside safe ranges.
AI/ML Platforms
Drift detection agents that watch your production models 24/7 and trigger retraining workflows automatically when performance degrades — no manual monitoring needed.
We don't install agents
and walk away. Here's the full picture.
Every engagement follows a structured sequence — starting with understanding your environment, not pitching a generic solution.
Map your toil before we write a single line
We spend the first week logging where engineering time is actually going — incidents, release prep, manual checks, repetitive fixes. We identify the three or four workflows where agents will have the biggest impact, ranked by frequency and engineer-hours saved.
Design agents around your actual tools and workflows
We build for what you already use — GitHub Actions, Jenkins, PagerDuty, Datadog, Grafana, Slack, Jira, your cloud provider. No new platforms required. Agents connect to your existing data sources and action points, not replace them.
Build in a 2–3 week pilot before the full engagement
One agent, one workflow, your real environment. You see it working before you commit to anything else. The pilot is paid, scoped, and produces something tangible — not a proof of concept sitting in a demo environment.
Expand to the full agent suite over 90 days
After the pilot validates the approach, we build out the agreed agent suite — each one reviewed by your team before going live. Your engineers stay involved throughout so they understand how the agents work and can modify them after handover.
Hand over runbooks, documentation, and full ownership at day 90
At the end of the engagement, you have production-running agents, full documentation, and runbooks for common scenarios. No ongoing Stonetusker dependency required. Optional support is available if you want it — on your terms.
One agent. Your stack.
Working results in 2 to 3 weeks.
Every engagement starts with a paid 2–3 week pilot. One agent, scoped to your highest-value workflow, built and tested in your real environment. You see the output before committing to anything else. If the pilot doesn't deliver, you don't pay for the next phase. That's in the agreement.
- Scoped to your single highest-ROI workflow
- Built on your actual stack — not a sandbox
- NDA signed before any technical discussion
- Your team reviews the design before we build
- Milestone billing — you pay for results, not hours
- Full documentation delivered with the pilot
What's different about
how we do this.
NDA before anything technical
Your architecture, incident patterns, and workflow data are confidential. We sign before you share a single diagram. Always.
Built on 26 years of delivery engineering
Subeesh has run delivery infrastructure for 300+ engineers across Nokia, Stryker, and Vocera. We build agents that fit real engineering teams — not ones that look good in a demo.
You own everything at the end
No black box. No ongoing dependency. Every agent comes with documentation and runbooks your team can maintain, extend, and modify without us.
Answered honestly.
We already use AI tools — Copilot, ChatGPT. How is this different?
Those are assistant tools — they respond when a human asks them something. Agentic AI is different: agents run autonomously, react to system events, and take action without a human triggering them. An incident triage agent doesn't wait to be asked — it fires the moment an alert comes in and posts its findings before your on-call engineer even reads the page. That's a fundamentally different capability.
Do we need to replace our monitoring or CI/CD stack to use this?
No. We build agents that connect to what you already use — Datadog, Grafana, GitHub Actions, Jenkins, PagerDuty, Slack, Jira. The whole point is to layer intelligence over your existing infrastructure, not replace it. If your stack is unusual, we'll tell you in the pilot assessment before we propose anything.
What if an agent does something wrong in production?
Every agent we build has explicit permission boundaries — it can act only within the scope you define and approve. For anything consequential, we design a human-in-the-loop confirmation step by default. Full autonomy is an option you turn on, not a default we push.
What does this typically cost?
Agentic AI engagements run $20K to $50K USD over 90 days, confirmed after the pilot. The pilot is separately scoped and priced. Use our Tusker90Pro tool to get a personalised estimate in about two minutes.
Can this be combined with the 90-Day DevOps Transformation?
Yes — and for teams already investing in DevOps transformation, adding agentic automation in the same engagement is the highest-leverage option. You build the pipeline first, then layer agents on top. We can structure this as a single engagement or as sequential phases, depending on your team's capacity and timeline.
Your engineers have better things to do
than be on-call for toil.
30 minutes. No pitch deck. We arrive having already mapped where agents would have the highest impact in your environment.
No retainers·No long-term contracts·NDA before any technical discussion