Why GitOps on a POC Cluster#

Setting up ArgoCD on minikube is not about automating deployments for a local cluster – you could just run kubectl apply. The point is to prove the deployment workflow before production. If your Git repo structure, Helm values, and sync policies work on minikube, they will work on EKS or GKE. If you skip this and bolt on GitOps later, you will spend days restructuring your repo and debugging sync failures under production pressure.

Advanced Git Operations#

These are the commands that separate someone who uses Git from someone who understands it. Each one solves a specific problem that basic Git workflows cannot handle.

Interactive Rebase#

Interactive rebase rewrites commit history. Use it to clean up a branch before merging.

# Rebase the last 4 commits interactively
git rebase -i HEAD~4

This opens an editor with your commits listed oldest-first:

pick a1b2c3d feat: add user export endpoint
pick e4f5g6h WIP: export formatting
pick i7j8k9l fix typo in export
pick m0n1o2p feat: add CSV download button

Change the commands to reshape history:

Interactive Rebase#

Interactive rebase rewrites commit history before merging a feature branch. It turns a messy series of “WIP”, “fix typo”, and “actually fix it” commits into a clean, reviewable sequence.

Start an interactive rebase covering the last 5 commits:

git rebase -i HEAD~5

Or rebase everything since the branch diverged from main:

git rebase -i main

Git opens your editor with a list of commits. Each line starts with an action keyword:

Agent Context Preservation for Long-Running Workflows#

The context window is the single most important constraint in agent-driven work. A single-turn task uses a fraction of it. A multi-hour project fills it, overflows it, and degrades the agent’s reasoning quality long before the task is complete. Agents that work effectively on ambitious projects are not smarter – they manage context better.

This article covers practical, battle-tested patterns for preserving context across long sessions, delegating to sub-agents without losing coherence, and avoiding context pollution – the gradual degradation that happens when irrelevant information accumulates in the working context.

Why Change Management Matters#

Most production incidents trace back to a change. Code deployments, configuration updates, infrastructure modifications, database migrations – each introduces risk. Change management reduces that risk through structure, visibility, and accountability. The goal is not to prevent change but to make change safe, visible, and reversible.

Change Request Process#

Every infrastructure change flows through a structured request. The formality scales with risk, but the basic elements remain constant.

Designing Agent-Ready Projects#

An “agent-ready” project is just a well-documented project. Every practice that helps an agent — clear conventions, explicit commands, tracked progress, documented decisions — also helps a new team member, a future-you who forgot the details, or a contractor picking up the project for the first time.

The difference is that humans can ask follow-up questions and gradually build context through conversation. Agents cannot. They need it written down, in the right place, at the right level of detail. Projects that meet this bar are better for everyone.

Git Branching Strategies#

Choosing a branching strategy is choosing your team’s speed limit. The wrong model introduces merge conflicts, stale branches, and release bottlenecks. The right model depends on how you deploy, how big your team is, and how much you trust your test suite.

Trunk-Based Development#

Everyone commits to main (or very short-lived branches that merge within hours). No long-running feature branches. No develop branch. No release branches unless you need to patch old versions.

Jenkins Pipeline Patterns#

Jenkins pipelines define your build, test, and deploy process as code in a Jenkinsfile stored alongside your application source. This eliminates configuration drift and makes CI/CD reproducible across branches.

Declarative vs Scripted#

Declarative is the standard choice. It has a fixed structure, better error reporting, and supports the Blue Ocean visual editor. Scripted is raw Groovy – more flexible, but harder to read and maintain. Use declarative unless you need control flow that declarative cannot express.

Long-Running Workflow Orchestration#

Most agent examples show single-turn or single-session tasks: answer a question, write a function, debug an error. Real projects are different. Building a feature, migrating a database, setting up a monitoring stack – these take hours, span multiple sessions, involve parallel work streams, and must survive context window resets, session timeouts, and partial failures.

This article covers the architecture for workflows that last hours or days: how to model progress as a state machine, how to checkpoint for reliable resumption, how to delegate to parallel sub-agents without losing coherence, and how to recover when things fail partway through.

Progressive Agent Adoption#

Nobody goes from “I have never used an agent” to “my agent runs multi-hour autonomous workflows” in one step. Trust builds through experience. Each successful task at one level creates confidence to try the next. Skipping levels creates fear and bad outcomes — the agent does something unexpected, the human loses trust, and adoption stalls.

This article maps the adoption ladder from first task to autonomous workflows, with concrete examples of what to try at each level and signals that indicate readiness to move up.