Playbooks
Repeatable delivery patterns captured into the knowledge base — so the whole team delivers at the speed of the best engineer.
Consulting margin depends on senior-hours, and senior-hours are the scarcest thing a services firm has. The quiet tax on that margin is repetition. Every job re-solves problems the team has already solved, because the solution lived in one engineer's head or one project's files instead of somewhere the next job could reach it.
Playbooks is the initiative inside Agentic Delivery that fixes this. It captures the reusable patterns of the work into a shared knowledge base, so a pattern is solved once and reused everywhere.
AI drafts, the engineer approves
Delivery is AI-assisted, and the split is precise. AI agents draft the mechanical output — configs, first-pass analyses, the intent behind a change. A senior engineer reviews that draft against the real environment and approves it. The AI does the writing. The human owns the decision.
AI as drafter
Agents turn intent into config and draft routine analyses. This is the AI-assisted layer, and Vizzy is the harness that runs it.
Engineer as reviewer
A senior consultant reviews every draft against the customer's architecture and security. The approval gate is where accountability lives.
Keep the line clear. The drafting is AI. The execution — the CLIs that carry a change into Cribl or Splunk — is rule-based automation: it runs the same way every time, covered under Remediate. Playbooks is about the drafting layer and the patterns that feed it.
Measured savings, not a promise
The point of the AI-assisted layer is time. Routine tasks that used to take an engineer an hour and a half drop to minutes. Turning a known intent into a known config is exactly the work an agent drafts well and a human can check fast.
The time savings are on the routine, repeatable work. Judgment, architecture, and the review itself do not shrink — and are not meant to. AI removes the grind. It does not remove the engineer.
What a playbook is
A playbook captures a reusable delivery pattern for one technology — the intent, the drafting approach, the review checklist, the known traps — and stores it in the knowledge base. The first engineer to solve a problem writes the pattern down as they solve it. Every engineer after that starts from the solved version.
Solve once
An engineer works through a delivery problem with AI help and captures the pattern as they go.
Capture into the knowledge base
The pattern — one per technology — lands in the shared knowledge base, not in one person's memory or one project's files.
Reuse everywhere
The next job pulls the drafted output from the saved pattern. The whole team delivers at the speed of the best engineer who has faced it.
This is what makes the economics get better with each job instead of resetting. A saved playbook is a lasting asset — the second delivery of a pattern costs a fraction of the first.
When the harness cannot go into the client environment
The standard AI harness cannot always run inside a customer's environment. Security policy, network boundaries, or tooling limits can rule it out. Playbooks are built to travel anyway.
Where the standard harness cannot run, an approved editor or an open-source CLI route delivers the same playbooks against the same knowledge base. The pattern does not change. Only the vehicle carrying it does. Standing up a working client-side setup targets roughly a month.
Why it matters
Agentic Delivery makes every engagement faster, and Playbooks is what makes it build on itself. Every job makes the next one faster, because the pattern is captured instead of worked out again — and the team delivers as one, at the speed of its best.