Agentic Team Training Scope

What your dev team learns in 4 weeks — and what they can do after.

Why This Engagement Exists

Your developers are already using AI — ChatGPT, Copilot, Claude. But they're using it like a search engine: ask a question, copy the answer, hope it works. That's vibe coding. It produces 20–30K lines of code per month with a 45% vulnerability rate.

Agentic workflows are different. They're structured systems where AI agents execute tasks autonomously — writing code, running tests, fixing failures, submitting PRs — with your developers overseeing the process instead of typing every line. The throughput gap is 150x.

This engagement sets up those systems on your codebase and trains your team to operate them independently.

Week-by-Week Breakdown

Week 1: Assessment & Architecture

Audit your current development workflow, CI/CD pipeline, and AI tool usage
Identify the highest-leverage automation points — where agents create the most value
Select the right LLM for each workflow: Anthropic (Claude) for deep code reasoning, OpenAI for breadth and tool use, Gemini for multimodal tasks
Design the agentic pipeline architecture for your specific stack and codebase
Deliverable: Architecture document with workflow diagrams, LLM selection rationale, and implementation plan

Week 2: Pipeline Build

Build the agentic coding pipeline on your real codebase — not a demo project
Set up agent orchestration: task decomposition, parallel execution, context window management
Configure test generation agents and automated review gates
Integrate with your existing Git workflow, CI/CD, and deployment pipeline
Deliverable: Working agentic pipeline that generates, tests, and submits PRs on your codebase

Week 3: Team Training

Hands-on training: your developers build real features using the agentic workflow
Prompt engineering for code generation — writing specs that agents execute reliably
Agent orchestration patterns — when to use single agents vs. multi-agent pipelines
Debugging agentic workflows — reading agent logs, diagnosing context window issues, fixing pipeline failures
Deliverable: Your team has built and shipped real features through the agentic pipeline

Week 4: Self-Improvement & Handoff

Build the feedback loop system: agents log decisions, analyze patterns, and auto-adjust behavior
Set up quality gates that catch regressions before they reach production
Configure autonomous error correction for test failures and build breaks
Document operating procedures and escalation paths
Deliverable: Self-improving system your team owns and operates independently

What Your Team Can Do After

Agentic Coding: Orchestrate AI agents that generate, review, and refine code through structured workflows
Autonomous Testing: Agent pipelines that generate test suites, run adversarial edge cases, and self-heal on failure
AI-Powered DevOps: CI/CD automation, deployment agents, infrastructure-as-code — with human gates at critical points
Self-Improving Systems: Feedback loops that learn from every test failure, code review, and production incident
LLM Selection: Choose and configure the right model for each task across Anthropic, OpenAI, and Gemini

What This Is Not

Not a workshop or slide deck — it's a hands-on implementation engagement
Not staff augmentation — we build the systems and train your team to own them
Not vendor lock-in — everything runs on your infrastructure with open-source tooling
Not theoretical — your team ships real features through the pipeline during the engagement

Prerequisites

A development team (3+ developers) with an existing codebase
Git-based workflow with CI/CD pipeline (we can help set this up in Week 1 if needed)
Developers who can write code and use a CLI — no AI/ML experience required
Access to at least one LLM API (Anthropic, OpenAI, or Gemini) — we help select and configure

Ready to Assess Your Team?

The team assessment is a single call where we map your current workflow, identify automation opportunities, and show you what the 4-week engagement looks like for your specific situation.