Which AI agents do you still use every week, and which ones faded out?
I’ve noticed my own AI usage has changed a lot over the past few months.
Claude still feels like the most complete ecosystem for serious work. The docs, artifacts, coding flow, and surrounding tools make it easy to keep using it for longer tasks.
GPT has also become hard to ignore again. The update speed is fast, and I keep finding myself coming back to it for quick thinking, writing, search-like tasks, and general daily use.
But a few tools I used to check out often have slowly faded from my daily workflow. Not because they are bad, but because they did not become a habit.
This is also why I’ve become more interested in the “routing” side of AI tools. Once different agents become useful for different jobs, the question is no longer just which model is best. It becomes: which model or API should handle which workflow?
That is the direction we are thinking about at EvoLink: making it easier to access and compare different AI models through one API, instead of treating every new model launch as a separate integration decision.
EvoLink: https://evolink.ai/?utm_source=community&utm_medium=artos&utm_campaign=ai_agents_weekly_habits
So I’m curious:
- Which AI agent or assistant do you actually use every week?
- Do you use different agents for different tasks?
- What tasks do you still prefer Claude, GPT, Gemini, Grok, Cursor, Codex, or other agents for?
- Which tools did you try during the hype, but eventually stop using?
I’m less interested in benchmark rankings and more interested in real habits.
I went all-in on Claude for coding a few months ago and barely touch ChatGPT now, but I still keep Perplexity around for quick research stuff — feels like it earned a permanent tab in my browser.
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The agents that stick for me are the ones with a repeatable loop, not the ones with the best demo. Claude/Codex for coding when I can keep the context tight, GPT for quick research and first-pass writing, and smaller models only when latency/cost matters more than judgment.
The ones that faded are usually the “agent that does everything” tools. If I cannot predict what it will touch, how much context it will burn, or how easy it is to review the diff/output, it becomes a novelty instead of a habit.
One practical filter: I trust an agent more when I can name the unit of work and measure the session afterward. “Fix this auth edge case” survives. “Go improve the app” usually turns into context soup.
The point ibiti made about habits is the real insight, tools stick when they plug into a repeatable workflow. One part of the AI stack I still think gets overlooked is the input layer. Everyone is busy optimizing which model to send things to, but the typing bottleneck between thought and prompt is real. Dictating prompts instead of typing them cuts filler naturally, speaking tends to strip out the waffle that typing encourages. I built DictaFlow for exactly this: hold-to-talk voice dictation that types into any app. dictaflow.io
I use AI Manus, Claude and GPT to achieve a balance result. I certainly use different agents for different tasks.
I still prefer using Claude, Manus and GPT for my daily task because I get best results every time.
I’ve noticed something similar on the dev side — I don’t really stick to one agent anymore, it’s more like different tools for different parts of the workflow (debugging, structuring logic, quick implementation ideas).
Curious if others are also moving from “one main tool” to a more task-based setup.
The “habit” point is the strongest part here.
Most AI tools do not fade because the model is bad. They fade because they never attach to a repeatable workflow. Once a tool becomes part of writing, coding, research, support, or data work, switching becomes harder.
For EvoLink, I’d be careful not to frame it mainly as “access and compare models through one API.” That sounds useful, but still a bit infrastructure-generic.
The sharper angle might be closer to workflow routing: helping teams decide which model should handle which job, based on cost, speed, quality, context length, and reliability.
That makes the product feel less like another model gateway and more like the decision layer behind AI workflows.
Good question. My rotation has settled to three, and the routing between them became the actual workflow pattern:
Claude stays for production code and architecture reasoning. When I need to audit a codebase for a specific pattern or debug something with deep context, Claude is the only one I trust for the real work.
ChatGPT got added back for the quick stuff — rewriting email drafts, brainstorming landing page copy, generating 10 variations of a subject line. The latency improvement made it viable for throwaway tasks that would waste Claude's context budget.
Perplexity is the surprise keeper for research queries. When I need to know the actual state of something like automated testing patterns in Astro or how WebGPU rendering works in different browsers, having inline citations saves the copy-paste-search loop.
The ones that faded: all the specialized wrapper tools that were a single model behind a thin UI. Devin, various agent platforms. They tried to own the whole workflow but the switching cost between their opinionated environment and just opening Claude directly was never worth it.
I'm still routing manually. At team scale I can see the appeal of a unified API, but for solo work the overhead of a router layer hasn't justified itself yet.