01
Prompt-Driven
Single-LLM features: summarization, classification, code autocomplete. Fast but shallow. No memory, no architecture awareness.
Where most teams are
Programs · Anthropic select partner
Agentic SDLC with Claude Code
AI software engineering at production grade.
Activate Get your AI Adoption audit reportThe thesis
Agents need specs, not prompts.
An AI agent without context is a confused intern. Hand it a prompt and you get generic code. Hand it a product definition, a system architecture, a functional spec, and a task list, and you get production-ready software.
This is spec-driven development. Your team writes the intent. Agents execute it. The SDLC compresses from weeks to hours, but only if the chain of context is complete: product, roadmap, architecture, spec, tasks, implementation, verification.
02
Workflow-Driven
Chained steps with human checkpoints. Better, but the human is still the bottleneck at every handoff.
LLMs orchestrated by code
03
Spec-Driven
Full vertical context: product, roadmap, architecture, spec, tasks, implement, verify. Autonomous execution at scale.
Agents deciding their own trajectories
Board-Level Visibility
From the engineering floor to the boardroom.
The program measures from day one. This layer turns that measurement into a board-ready story: a composite Index, an ROI read, and a cost signal the leadership team can act on. Reported continuously, evidenced by your own systems.
Adoption & progress
How far along is the transformation? The Index tracks Adoption, Depth, and Outcome. Weighted by difficulty, evidenced by your data.
Cost governance
AI token spend shown as a share of the engineering budget: bounded, honest, and comparable quarter over quarter. Healthy and Warning scenarios side by side.
Per-team audit
Every team scored against the same 7-dimension rubric. The AWOS audit is the Depth engine: a defined, weighted, open methodology Provectus runs on your real codebase.
Sample board view
Agentic SDLC · Board-Level Visibility
Enterprise AI Transformation: Board Review
The AI Maturity Index
One composite number on a 0–100 scale, measured continuously. Same rubric every quarter.
Baseline 30
Current 55
Target 75
0 100 · the Index
How the 55 is built
12
20
23.4
Adoption · 15%
Depth · 40%
Outcome · 45%
· remaining headroom to 100
Weighted by difficulty: basic adoption is easy (low weight); delivery outcomes are hardest and matter most (highest weight).
Where the engineering budget goes, and whether AI is paying off
AI tokens shown as a share of total budget (not growth-from-zero). The reporting shows both outcomes, so it can tell the truth, not just good news.
Healthy: AI paying off
Maintenance share shrinks, feature capacity grows, tokens stay a thin sliver.
Warning: spend without payoff
Tokens balloon to ~14% of budget, maintenance stays stuck, feature capacity falls.
New-feature capacity
Maintenance / run
AI tokens (% of budget)
How to read it: each bar is 100% of the engineering budget.
Healthy: maintenance (grey) shrinks, freeing budget for new features (green);
the AI-token sliver (orange) stays small (~4%).
Warning: the token band balloons to ~14%, maintenance stays stuck, and
feature capacity actually falls. Same chart, opposite verdict: that's what makes it a steering
instrument, not a vanity metric.
CTO View: The Index, unpacked
Adoption 15% 80/100
Breadth How many licensed engineers actually use AI, week to week.
Trust How often AI's suggestions are good enough to keep.
Productive output Whether usage produces real commits and PRs, not just chatter.
Depth 40% 50/100
This layer is the AWOS audit: the open methodology Provectus runs today. Its 7 dimensions run from agent-navigable code up to autonomous execution. Every dimension is a defined, weighted, evidenced check.
1 · Code Architecture Clean, modular code agents can navigate and change safely.
2 · Agent Context & Docs The context agents need to act (CLAUDE.md, decisions, knowledge), kept current.
3 · Test & Verification Coverage Automated tests that let agents move fast and self-verify their work.
4 · AI Tooling & Reuse Skills, agents, and integrations built once and reused across teams.
5 · Spec-Driven Practice Work expressed as specs that drive agents, not one-off prompts.
6 · Agentic Autonomy Frontier Long-running, self-verifying agent loops running safely with oversight.
7 · Token Efficiency Disciplined AI spend: caching, right-sized models, low waste.
Red rows show the Index can go down. That's what makes the green credible.
Outcome 45% 52/100
Release velocity How much shippable value the team ships per quarter.
Cycle time How long an idea takes to reach production.
Maintenance burden Share of effort spent on bugs and support instead of new work.
How it works: your systems in, executive outcomes out
Data sources Analysis Consumers & outcomes
Data sources
GitHub · delivery
Jira · backlog
Claude telemetry · adoption & cost
+ extensible (e.g. support systems)
Analysis
Provectus agentic workflows
Combine · analyze · score, continuously
produces Index · ROI · Cost
Consumers & outcomes
CTO · CIO · Board
decide & sponsor
partner
Provectus FDX + FDE
advise & operate
Outcome, delivered together
Index ↑ · ROI ↑ · Cost ↓
For the boardroom
Board-Level Visibility is the link between your engineering transformation and the executive agenda. The Cowork Activation program takes the Index and ROI report to the board, shaping AI strategy and governance at the leadership level.
What we deliver
Five tracks. Stackable. Each priced before it starts.
Track 1
Foundation
101-301 for builders and leaders
Your engineering team gets hands-on with Claude Code on your real codebase. Not a demo repo. Three curriculum tiers: introduction, intermediate, advanced. From model selection and CLAUDE.md to sub-agents, hooks, custom skills, and parallel execution. Includes a baseline AI-Adoption audit on your real codebase. That sets the starting Index against which every subsequent track is measured.
Track 2
Advanced Claude Code
From fluent to frontier
The advanced modules that turn fluent users into power users. Plugins and marketplace. Sub-agents and parallel execution. Context engineering and model selection. Eval-driven development wired into CI. Autonomous execution with harnesses. Plus modernizing existing codebases so agents can navigate and change them safely.
Track 3
Enterprise Activation
Deep activation, team by team, project by project
Going deep where the work happens: each team and each project activated on the agentic SDLC, not just trained on it. Existing codebases brought from pre-AI to post-AI: refactoring, context engineering, and per-team enablement. Greenfield teams start agentic from day one. Wired into your stack: marketplace, plugins, MCP servers, skills, and integrations with Slack, Confluence, and your toolchain.
Track 4
Governance and Security
Enterprise-grade guardrails for agentic development
Permissions, sandboxing, and audit controls for autonomous agents running in production codebases. Security review workflows. Token cost governance. Compliance-ready policies for agent access to internal systems and data.
Track 5
Beyond Engineering
Product and business teams in the agentic SDLC
Engineering is the starting point, not the boundary. Product managers spec with agents. Business analysts build workflows. QA teams verify autonomously. The entire product delivery chain operates on the same agentic method.
What ships with the program
The Anthropic stack, configured for your team.
Claude Code
Frontier coding agent that plans, acts, and reflects autonomously. Reads your codebase, edits files, runs commands, ships features.
AWOS
Agentic Workflow Operating System. Spec-driven development framework that transforms Claude Code from a chat interface into an autonomous engineering department.
Skills + plugins
Reusable capabilities your team builds once and shares across the organization. Brand standards, API patterns, test formats, deployment runbooks.
MCP servers
Model Context Protocol integrations connecting agents to your systems: Git, CI/CD, databases, monitoring, Slack, Confluence, and custom internal tools.
The Claude Effect
The measurement framework that instruments your SDLC and produces the board-level Index, ROI read, and cost signal. Turns token data into a story your leadership can act on and your board can hold you to.
How it works
The details.
Format
Tracks run on your codebase, not a demo repo. Hands-on pairing with Provectus Forward Deployed Engineers (FDE) and a Forward Deployed Executive (FDX) who owns the board narrative.
Who
VP Engineering, Head of Platform, Head of Developer Experience, and engineering teams standardizing on AI-native development. The visibility layer serves the CTO, CIO, and board who need to see the ROI.
Timeline
2 weeks per track, five tracks stackable.
What your team leaves with
A production-grade agentic SDLC running on your codebase. AWOS configured. Agents shipping real work. A board-ready Index and ROI report evidenced against the pre-engagement baseline.
Your SDLC is about to change. The question is whether you lead the shift or absorb it.
Start with the Foundation track. Two weeks on your codebase. Your team ships real work on Claude Code.
Activate