AI Coding Agents Advanced English Founding Price

10X Agentic Engineer: 1:1 Coaching

Name: 10X Agentic Engineer: 1:1 Coaching
Price: 55000 INR
Availability: InStock

One-to-one: set up a virtual team of AI coding agents on your own codebase and lead it across the whole software lifecycle.

Designed & taught byDinesh Kushwaha FormatLive 1:1 online via Zoom

Duration: 8 weeks · 16 sessions × 2 h (32 hours)
Schedule: Two sessions a week, at times that suit you
Cohort: One-to-one: limited coaching slots each month
Taught in: English

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This course includes

16 live 1:1 sessions, recorded, with lifetime LMS access
Can be taught on your own codebase and tools
The Agent Team Starter Kit: role prompts, instruction files for all four tools, sub-agent and Skill templates, review and security checklists, CI templates
Direct support, plus 4 weeks of follow-up after the course
Capstone review and certificate of completion
GST invoice and an employer briefing for company-sponsored seats

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What you'll walk away with

Know how coding agents work: context, tools, the agent loop, MCP and permissions
A virtual team of 8 role agents set up in your repo
Requirements, architecture docs and ADRs produced with agents
Features delivered by parallel developer agents, reviewed by you
Automated tests, AI code review and a security gate on every PR
A CI/CD pipeline and a versioned release with notes and rollback
A portfolio-ready capstone shipped end to end

Who it's for

Software developers, freshers and final-year students, testers and SDETs, DevOps engineers, tech leads and engineering managers who want to be 10X more productive with AI coding agents. Comfortable with Git and at least one programming language.

Curriculum

Learn how coding agents really work, then run a virtual engineering team of AI agents (product manager, architect, developers, tester, code reviewer, security reviewer, DevOps engineer and release manager) across the whole software lifecycle. One real product is built through the course, and you finish as a forward-deployed engineer who can turn a customer brief into shipped software in days.

Week 1 · Sessions 1–2: How coding agents work & context engineering

Understand what happens inside a coding agent, then give every tool the same project knowledge.

LLMs, context windows, tool calling and the plan-act-observe loop; sandboxes, permissions and MCP
Where GitHub Copilot, Cursor, Claude Code and Codex differ, and when to use each
Repo instruction files (CLAUDE.md, AGENTS.md, Cursor rules, Copilot instructions), memory, custom commands and Skills

You build: All four tools set up on one repo, with a shared instruction pack they all read.

Week 2 · Sessions 3–4: Your virtual team & the Product Manager agent

Create one agent per SDLC role, then let the Product Manager agent turn a messy brief into a backlog.

Role agents with their own prompts, tools and definition of done; delegation and hand-offs
Parallel agents with git worktrees and sub-agents
PRD, user stories and acceptance criteria; Jira or Azure DevOps through MCP

You build: Eight role agents in your repo, a PRD and a backlog of 10 stories.

Week 3 · Sessions 5–6: Architect agent & Developer agents I

Design before you build, then deliver stories as reviewed pull requests.

Architecture options and trade-offs, ADRs, Mermaid diagrams, API contracts and data models
Story to PR with Cursor agent and GitHub Copilot agent mode
Reviewing large AI diffs quickly and safely

You build: An architecture doc, 3 ADRs, an OpenAPI spec and the first 3 stories as PRs.

Week 4 · Sessions 7–8: Developer agents II & Tester agent

Run several developer agents in parallel, then make quality automatic.

Parallel Codex cloud tasks, legacy refactoring, migrations and debugging with agents
Test strategy, TDD with agents, unit and integration tests
Playwright end-to-end tests and fixing flaky tests

You build: Three stories delivered in parallel, a legacy refactor, and a test suite at 80% coverage.

Week 5 · Sessions 9–10: Reviewer agent & Security reviewer agent

Put an AI reviewer and a security reviewer on every pull request.

Automated PR review: Copilot code review, Claude Code GitHub Action, Codex review
OWASP Top 10, secrets and dependency scanning, threat modelling
The risks agents bring: prompt injection, over-broad permissions, unsafe commands

You build: AI code review and a security gate running on every PR, plus a threat model.

Week 6 · Sessions 11–12: DevOps engineer agent & Release manager agent

Automate the path to production and release with confidence.

CI/CD with GitHub Actions or Azure DevOps, Docker and Terraform
Agents running headless inside pipelines
Semantic versioning, changelogs, release notes, feature flags, rollback and incident triage

You build: A pipeline from PR to staging, and release v1.0 with notes and a rollback plan.

Week 7 · Sessions 13–14: Forward-deployed engineering & measuring 10X

Work like a forward-deployed engineer: from customer brief to working prototype in days.

Discovery, rapid prototyping and integrating with a customer's systems
Productivity metrics (cycle time, DORA), cost control and governance
Rolling agents out to your team; choosing the right tool for each task

You build: A customer prototype built live from a brief, and your before/after productivity report.

Week 8 · Sessions 15–16: Capstone

Ship your own product idea end to end with your virtual team, then demo it.

Requirements, architecture, code, tests, security, pipeline and release
Code walkthrough and review by Dinesh
Portfolio write-up for GitHub and LinkedIn

You build: A portfolio-ready repo, a live demo and the 10X Agentic Engineer certificate.

Tools & technologies

Claude CodeOpenAI CodexCursorGitHub CopilotMCPGit worktreesPlaywrightGitHub ActionsAzure DevOpsDockerTerraformCodeQL

Your instructor

Dinesh Kushwaha

Founder, Dinesh AI Academy · Principal Software Engineer & Application Architect

Dinesh has spent 18+ years architecting and delivering 50+ enterprise-scale platforms and has coached more than 1,000 engineers. He teaches AI the way he builds it: practical, secure, and focused on what works in production.

Read Dinesh's story

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