AI agents are moving past the point where generating an answer is enough. The real question is whether an agent can stay inside a real environment long enough to actually finish the job. That’s what makes Nex-N2.5 interesting to me It is built around long-horizon computer use, with agents... that can operate browsers and software, execute programs, see the results through visual feedback, and keep correcting their work instead of stopping at the first output. Nex-N2.5 Pro was put through exactly that kind of workflow: build a SQL interpreter, write tests, run it, identify what was missing, implement the next pieces, and keep iterating. The model progressively handled things like WHERE, JOIN, subqueries, qualified names and table aliases while testing its own implementation along the way. That is the difference I care about with agentic AI. The agent isn't just producing an artifact. It's operating inside a feedback loop: write → run → inspect the result → fix the problem → verify again. Nex-N2.5 is also released as a family of Mini, Pro and Max models, giving developers different options depending on the task and deployment requirements. The Nex-N2.5 Mini and Pro are both available through OpenRouter for free for a limited time: Try it out here: The real shift isn't just better AI outputs. It’s agents that can interact with an environment, respond to what happens, and keep working until the task is actually finished.show more

This is the part I actually care about. Not another first-try answer, an agent that stays in the tool, sees the result, and keeps fixing until the work is finished.

The SQL interpreter example is the right kind of test. First output is easy. Staying in the environment long enough to add WHERE, JOIN, tests, and fixes is the actual work.

The write → run → inspect → fix loop is the part that matters. Generating a SQL interpreter once is easy. Staying in the environment long enough to add JOIN, aliases, and tests is the actual test.

Building a working SQL interpreter step-by-step through self-testing is a massive flex for a long-horizon agent. Way more impressive than just spitting out a static script on the first try.

The write-run-fix cycle is where developers spend most of their time anyway, so having an agent that can handle that loop autonomously is a massive time-saver.

generating an answer isn’t the bar anymore. staying in the environment long enough to finish the job is.

Write → run → inspect → fix is a cleaner way to judge these models than a benchmark screenshot. If the agent drops out after the first artifact, it is still just a generator.

That closed-loop execution is honestly the real breakthrough. Most models just hand you code that breaks instantly, so watching an agent actually test and debug its own SQL logic changes everything.

Perfectly put. We're finally shifting away from glorified autocomplete into actual autonomous execution that can see a multi-step task through to the end.

Visual feedback plus self correction is the piece people keep asking for. An agent that can see what broke and keep iterating is more useful than one that only writes code once.

This is the part of agentic AI I find most interesting: not just generating an answer, but being able to interact with the environment, check the result, and keep improving until the task is actually done.

The real test for AI agents isn’t how impressive the first response looks. It’s what happens when things go wrong. Being able to detect an error, adapt, and keep going is where agentic systems can become genuinely powerful.

The feedback loop part is the real upgrade. Write → run → fix → verify is what I actually want from agents.

Visual feedback matters more than people admit. An agent that cannot see the result is guessing. An agent that can inspect the run and patch the next piece is doing work.

The write → run → inspect → fix → verify loop feels like a much more meaningful benchmark for AI agents than simply asking whether the first output looks good. That’s where things start getting really interesting.

The real upgrade is the feedback loop, not just stronger outputs. Agents that can test, inspect, correct, and verify are much closer to actually completing tasks.

Many of the most important contributions happen quietly before the community sees the results.

The clip is doing the talking. You can watch it work inside the software, check the view, correct the path, and keep going instead of stopping at the first pass.

this is the part that actually matters. generating the first draft is easy. staying in the loop and finishing the job is the hard part.

Sounds like Nex-N2.5 has some serious potential, bro! Excited to see where it goes.

Build SQL → test → find gaps → implement. WHERE, JOIN, subqueries handled. This is long-horizon Computer Use 👀

This is the future of AI—no more good enough, just relentless iteration until it’s actually done. NexN2.5 doing SQL dev like a real engineer is wild. Hope they don’t break my laptop in the process.

I’m more interested in crypto when the technology is solving real problems instead of chasing hype.

long horizon browser use and visual feedback sounds key for finishing

The write-run-fix loop is exactly where I've seen most agents fall apart. They dump code and bail. Curious how Nex-N2.5 handles edge cases when the SQL interpreter hits something it's never seen before though. Self-testing is cool until it gets stuck in a loop.

Longhorizon tasks require more than just generation, they need persistence. What counts isn’t answering but whether the agent stays engaged long enough to complete it

Finally something that acts like a real worker

Building a SQL interpreter is an elite benchmark. Did you notice any major performance throttling or heavy latency during the iteration cycles?

Mini, Pro, and Max is a practical detail too. Not every task needs the biggest model. Cost and latency decide whether this stays a demo or becomes usable.

The SQL interpreter demo is the part that actually lands. Anyone can dump a first-pass parser. But watching the agent write tests, run them, notice missing JOINs and aliases, then keep going until the thing works is a different category of capability.

Long-horizon computer use is the test that matters. This one operates the software, looks at what happened, and keeps working.

Static generation gets you 80% of the way there; closed-loop execution is what finishes the job. Being able to iterate against an actual environment changes the entire game for agentic workflows.

This is more than a agent for everyday work this is needed for essential and strong tasks. Amazing

The SQL interpreter example is the cleanest version of that. It didn’t just generate WHERE and JOIN. It kept adding pieces after it saw the tests fail.

Finally AI that actually gets stuff done