Programs · Anthropic strategic partner

Agentic SDLC with Claude Code

AI software engineering at production grade.

The imperative

AI coding tools are evolving faster than your SDLC.

After autocomplete and AI-native IDEs, the next shift is here: coding agents that plan, act, and reflect autonomously. They read your codebase, edit files, run commands, and ship features.

The tooling is ready. The question is whether your engineering organization can absorb it. Most cannot. The gap is not the tools. The gap is the method: how teams spec work, how agents receive context, how code moves from intent to production.

The 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.

Provectus built the method. We call it AWOS, the Agentic Workflow Operating System. It ships with this program.

Prompt-Driven

Single-LLM features: summarization, classification, code autocomplete. Fast but shallow. No memory, no architecture awareness.

Where most teams are

Workflow-Driven

Chained steps with human checkpoints. Better, but the human is still the bottleneck at every handoff.

LLMs orchestrated by code

Spec-Driven

Full vertical context: product, roadmap, architecture, spec, tasks, implement, verify. Autonomous execution at scale.

Agents deciding their own trajectories

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.

Track 2

Enterprise Enablement

From Claude Code to an agentic SDLC

Spec-driven development with AWOS. Claude Harness for autonomous execution loops. Observability: TokenOps and security. Ecosystem setup: marketplace, plugins, MCP servers, skills, agentic teams. Pre-configured integrations with Slack, Confluence, and your existing toolchain.

Track 3

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 4

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.

Track 5

Legacy SDLC Modernization

Brownfield to post-AI in production

Legacy teams and projects modernized from pre-AI to post-AI state: refactoring, context engineering, team enablement. Greenfield product teams enabled on agentic workflows.

What ships with the program

The Anthropic ecosystem, 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.

How it works

The details.

Format

Tracks run on your codebase, not a demo repo. Hands-on pairing with Provectus Forward Deployed Engineers.

Who

VP Engineering, Head of Platform, Head of Developer Experience, and engineering teams standardizing on AI-native development.

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. Adoption and output measured against a 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.

Start a Sprint