• 2026: AI Editor → Agent Command Center
• What's Next?
  • Better Agent Harnesses
  • Proactivity: Can Agents Suggest What to Do Next?
  • Team Collaboration
  • Verification: Can an Agent Verify if a Change Actually Works?

2026: AI Editor → Agent Command Center

As of 2026, there are four major forms of agentic coding products:

We can split them into two phases:

Phase 1: Transition from human → agentic coding (2023 ~ 2025):
Products: Editor & IDE + chat side-panel, Coding Agent CLI

• Editors predate LLMs, adding a side-panel enables AI coding without breaking the classic interface.
• Coding agent CLIs were first built as an experiment (according to Claude Code's creator). Nobody thought they would work this well, so the creators chose a CLI over a GUI simply because it was easier to build.

Phase 2: 🤖 Agent-native workflows (2025 ~ now):
Products: agent coworker chat apps, agent command center apps

Compared to Phase 1—where humans still wrote code by hand or constantly steered the agents—the primary user journey has shifted to orchestrating agents to handle work from start to finish. The agent is no longer an assistant living in a side panel; it represents the core functionality of the product.

In 2026, there is a notable trend: companies are redesigning their existing products to be agent-focused. For instance, Google launched Antigravity 2.0 at I/O, replacing the VS Code-based editor with an agent-first layout (i.e., an agent command center). Cursor 3 did this even earlier in April. To quote from their announcement:

We're introducing Cursor 3, a unified workspace for building software with agents. The new Cursor interface brings clarity to the work agents produce, pulling you up to a higher level of abstraction, with the ability to dig deeper when you want. It's faster, cleaner, and more powerful, with a multi-repo layout, seamless handoff between local and cloud agents, and the option to switch back to the Cursor IDE at any time.

This shift is more significant than you might realize. Leading players in the market have thrown away incredibly successful products used by millions of people to create something completely new. Clearly, these are rational, careful decisions backed by comprehensive internal studies and metrics. Is this the final form of agentic coding products? Not necessarily, but for the near future, agent command center will continue to dominate.

What about agent coworker chat apps? I love the concept, and I use OpenClaw from Telegram every day. But the question remains: do I really want another chat app? Maybe not. That's why I think WhatsApp, Telegram, Slack, and Discord will maintain their positions while becoming more AI-native. Besides, a good coding environment still requires features like code editing, debugging, and linting, which chat apps don't provide out of the box.

What's Next?

Today, if you use Codex/Cursor/Antigravity, they look and feel almost identical:

Antigravity 2.0

Codex App

Cursor 3

A natural question is: what's next? I see a few directions for evolution in agent capabilities and product design:

Better Agent Harnesses

Needless to say, agents harnesses will continue to improve. There is a ton of exciting research into dynamic context, safer sandboxes, memory/skill management, and subagents. What I find most interesting is that Anthropic "abandoned" their belief that "You don't need a new agent, just create skills," and instead created agent teams and dynamic workflows. If I understand correctly, this is a loop:

• Previously, when there weren't enough agent trajectories in the training data, you needed a harness layer or prompting tricks to make things work. Now, as LLMs are trained directly on that data, they can complete long-running, multi-tool tasks without much steering, allowing the harness layer to become thinner and more standardized.
• Consequently, people have created more sophisticated architectural patterns like agent teams because LLMs aren't inherently trained to handle them yet. Will LLMs handle this natively someday, creating a harness on the fly to fit a given task? It might not be that far off.

Proactivity: Can Agents Suggest What to Do Next?

People have been talking about proactive agents for a while, but few products actually support them. Jules is one of the exceptions. If you haven't tried this feature, Jules scans your codebase looking for TODOs and performance bottlenecks, generates suggestions, and notifies you. Once you approve a suggestion, Jules automatically creates a PR. I find the performance tips quite useful and have already merged multiple PRs this way.

Beyond that, agents should be able to proactively maintain a codebase by completing tasks such as:

• Fixing code style issues
• Creating tests
• Upgrading dependencies
• Fixing bugs

Some tasks may require external triggers, like a pipeline that automatically converts GitHub issues into user prompts. What is even more interesting—and has yet to reach a consensus—is whether we should let agents proactively suggest new features, let alone submit those changes without human approval. This is very different from standard maintenance work, as the problem space is open-ended with no single right answer. I'm looking forward to hearing stories from teams trying this.

Team Collaboration

Imagine a team working like this: previously, engineers produced 5 commits a day; with coding agents, they produce 12.

This brings two major challenges, which will only intensify as coding agents become more capable:

• Context Isolation: Vital engineering context gets trapped inside isolated agent conversations, leading to a breakdown in shared team knowledge. With engineers shipping code they don't read, even humans lose the full context of how the code functions.
• Coordination Hurdles: Codebases evolve 10x faster with AI. Without a central hub to synchronize intent and progress, teams face broken APIs, redundant efforts, and code conflicts. Existing coordination problems are magnified tenfold.

A possible solution is to build a coordination layer that syncs context across agents and accumulates a team memory. This would act as your team's ultimate PM—someone who knows what everyone is working on and proactively unblocks or helps people when needed. Much like the human brain, long-term memory would store lasting team knowledge (e.g., design choices), while short-term memory tracks immediate team progress and workspace status. SageOx is one startup embracing this exact idea.

Verification: Can an Agent Verify if a Change Actually Works?

I'm putting verification at the end, as it is always the final step in an agent loop (if there is one) 😉.

Many people don't realize how important verification is. However, if you examine agent trajectories, almost all coding agents perform some form of validation at the end. For example, an agent might launch a local server to test a newly added endpoint. If a test fails, it will attempt to fix the issue and continue looping until it passes.

Does this mean verification is a solved problem? Certainly not. The biggest bottleneck remains setting up the test environment. Not all verification is as simple as sending HTTP requests. Changes to a frontend, for instance, typically require browser automation or even recording video and screenshots to be evaluated by an LLM auto-rater—which is a non-trivial process on its own. What if you're fixing bugs for an Android app but don't have the necessary toolchains installed? Yes, you can connect to a remote sandbox containing all dependencies, but then you have to figure out communication protocols and address privacy concerns. While it isn't too difficult to build a custom solution for a specific requirement, turning this into a generic, out-of-the-box component for any coding agent still requires a long way to go.