Blog
@nikopetrov
TL;DR
"I tested the future of GitHub Copilot autonomous agents worktrees 2026. See how isolated agent sessions and IDE management change coding. My honest review and workflow insights."
I have been experimenting with the idea of autonomous coding agents for a while now. The recent buzz around GitHub Copilot's 2026 vision, particularly the discussions on agentic workflows and Worktrees for Isolated Agent Sessions, really piqued my interest. It felt like something I needed to get my hands on, even if it meant simulating future capabilities.
The YouTube videos, like "The Architecture of Autonomy Unpacking the 2026 GitHub Copilot" and "Agentic Workflows Have Changed EVERYTHING in 2026 (DEATH Of The Senior Dev?)," paint a picture of a dramatically different development environment. My goal was to see if I could replicate some of these proposed agent driven workflows today, or at least understand the underlying concepts well enough to prepare. Honestly, I was skeptical about the "death of the senior dev" claim, but I was excited about the potential for efficiency gains.
One of the core concepts presented is the "Agent First Workflow" and how current IDEs create IDE Bottlenecks For Autonomous Agents. Right now, when I use GitHub Copilot or Cursor Editor, it's mostly about code completion, chat, and perhaps generating a small function. But an autonomous agent needs more. It needs a sandbox, a way to experiment, to fail. And to report back without messing up my main branch.
The idea of a Standalone GitHub Copilot App is genuinely interesting here. It suggests a dedicated environment, not just an IDE extension. I started thinking about how I manage parallel development tasks. Typically, I use separate git branches, maybe even separate local clones for complex feature work or bug fixes. An agent, if truly autonomous, would need its own isolated space to work, run tests, and iterate.
My initial thought process went like this:
This led me down the path of exploring git worktrees as a manual, human driven precursor to what the 2026 Copilot might automate. I remembered reading about git worktrees a while back but never really incorporated them into my daily workflow. This felt like the perfect opportunity.
The concept of Worktrees for Isolated Agent Sessions immediately clicked. A git worktree allows you to have multiple working directories associated with the same repository, each on a different branch. This is exactly what an autonomous agent would need: its own clean slate, its own branch, its own set of files, all while sharing the underlying repository history.
Here's how I set up a basic "agent session" using worktrees. I'll simulate a scenario where an agent is tasked with adding a new API endpoint.
First, I start in my main project directory:
mkdir agent_project
cd agent_project
git init
echo "print('Hello from main')" > main.py
git add .
git commit m "Initial commit"
Now, I create a new worktree for my hypothetical agent. I'll call the branch agent/add api endpoint. This creates a new directory, ./worktrees/add api, containing a copy of the project at that new branch.
git worktree add ./worktrees/add api agent/add api endpoint
If I switch into that worktree directory, I am on the agent/add api endpoint branch, completely isolated from my main branch:
cd worktrees/add api
git status
# On branch agent/add api endpoint
nothing to commit, working tree clean
Now, imagine my GitHub Copilot powered agent is running inside this ./worktrees/add api directory. It could be using a tool like Aider or even just scripting direct git commands. It would then start modifying files, adding it's API endpoint code, running tests. And committing its changes.
For example, the agent might generate this:
# worktrees/add api/api.py
def get_status(): return {"status": "ok", "version": "1.0.0"} # worktrees/add api/main.py (modified by agent)
import api
print('Hello from main')
print(api.get_status())
The agent would then stage and commit its work:
git add .
git commit m "feat: add status API endpoint [agent id:123]"
This is the interesting part: the isolation. My main project directory remains untouched. The agent can break things, make mistakes, or even generate garbage, and my primary development environment is safe. This manual setup gave me a real appreciation for the potential of true AI Coding Guide automation in this area.
Once an agent has done its work in an isolated worktree, the next step is Agent Merge And CI Repair. This is where the vision gets really powerful. After the agent commits its changes, the system would ideally:
This "CI Repair" loop is fascinating. Instead of a human developer wading through logs, the agent is given another chance, another context. And told to fix its own mess. It's like having a junior developer who learns from their mistakes in real time, without needing constant supervision.
I simulated this by manually checking out the agent's branch, running a test that I knew would fail, and then prompting a general purpose LLM like ChatGPT or Claude Code with the error message. It's not fully autonomous, but it gives a glimpse.
Here's a simplified imagined flow for CI repair:
# Imagine a CI system detects a test failure
# Agent receives: "Test failed in api.py, 'version' key missing" # Agent then attempts a fix
# It might modify api.py:
# From:
# return {"status": "ok", "version": "1.0.0"}
# To:
# return {"status": "ok", "api_version": "1.0.0"} # Fixing a hypothetical naming convention error # Agent commits fix
git add api.py
git commit m "fix: address missing 'version' key in API response [agent id:123 retry1]"
# CI re runs.. This feedback loop, if truly automated and effective, would be a game changer for developer productivity. It would mean less time spent on trivial bug fixes and more time on complex problem solving.
The videos also highlighted Agent First Workflow: Session Management in VS Code and an Agents window introduction. This is crucial. For these agentic workflows to be usable, the IDE needs to provide clear visibility and control.
My current workflow with tools like Obsidian AI or Notion AI for organizing thoughts, or Raycast AI for quick commands, is very human centric. The agent window concept suggests a dedicated panel where I can:
This kind of transparent session management is exactly what is missing from current autonomous agent experiments, which often feel like black boxes. I want to see the agent's scratchpad, its internal monologue, and its reasoning. That's how I build trust.
Honestly, without a clear UI, managing multiple agents in different worktrees would quickly become a nightmare. I would probably lose track of which agent is doing what, especially if I had several running concurrently. This is where Microsoft Copilot and its deeper VS Code integration have a real advantage.
The provocative title, "Agentic Workflows Have Changed EVERYTHING in 2026 (DEATH Of The Senior Dev?)" naturally generated a lot of discussion. My honest reaction? No, I don't think so. Not in the way people imagine.
What I do see changing is the nature of a senior developer's work. Instead of spending time on boilerplate, debugging minor CI failures, or writing basic utility functions, senior devs will become orchestrators and architects of these agentic workflows. Their role will shift to:
Think of it less as replacement and more as augmentation. Tools like Make (Integromat) or n8n already show how automation can elevate human capabilities, not replace them entirely. The senior dev will still be the one steering the ship, just with a much more powerful crew.
While playing around with these ideas, I had a few "Today I Learned" moments:
git worktree, pytest, and can modify files in your current directory" would be crucial.This line of thinking also reinforces the value of tools like Claude Code and DeepSeek which are designed to understand and generate large codebases, providing a stronger foundation for agent reasoning. The ability to parse context, propose changes. And even self correct is paramount.
You can see how this differs from just simple code generation. It's about automating the entire development loop, from task decomposition to testing to integration. The future of AI coding is not just about writing code faster, but about building intelligent systems that can contribute to the codebase themselves.
While we don't have the 2026 GitHub Copilot autonomous agents just yet, you absolutely can experiment with git worktrees and current AI coding tools to simulate these workflows. It gives you a real feel for the benefits and the challenges.
For a basic agent driven workflow:
git worktree add ../agent_work agent_feature_branch../agent_work, use your preferred AI code assistant (ChatGPT, Claude Code, Cursor Editor) to make changes based on a clear task.git diff main agent_feature_branchIt's a manual process, but it highlights the primitives that future autonomous agents will build upon. Seeing how the isolation benefits your main codebase is a powerful insight.
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GitHub Copilot autonomous agents, in their 2026 vision, are expected to use git worktrees to create isolated development environments. This allows an agent to work on a specific task or feature branch without affecting the main codebase, enabling safe experimentation, testing, and error recovery before changes are merged.
Current IDEs often present bottlenecks for AI coding agents due to their human centric design. Agents need dedicated sandboxes, clear visibility into their operations, streamlined ways to interact with the file system and version control, and specialized UI for session management, which standard IDEs lack.
No, AI coding agents are unlikely to fully replace senior software developers. Instead, they will augment developer capabilities by automating repetitive tasks, boilerplate code. And initial debugging. Senior developers will likely evolve into orchestrators, architects, and reviewers of agent generated code, focusing on higher level problem solving, system design. And agent refinement.
Agent Merge and CI Repair refers to the proposed future capability where autonomous agents can automatically create pull requests from their worktree branches, trigger CI/CD pipelines, and then, if tests fail, attempt to fix their own code errors. This creates a self correcting development loop, reducing human intervention in debugging.
Yes, you can experiment with agentic workflows today by manually combining git worktrees with existing AI coding assistants like ChatGPT, Claude Code, or Cursor Editor. By creating isolated worktrees for specific tasks and using AI to generate and modify code within them, you can simulate the core benefits of future autonomous agents.
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