Despite the title, the article is not making claims of, or demonstrating general usage improvements or general coding agent improvements
With an assemblage of models like Jev + Opus or Jev + Qwen, Jev would often do the right thing when deciding to escalate to another model - I was impressed that it would usually make the right call, especially if you gave it good awareness of what the other model capabilities were. "ESCALATE to opus if you need good vision awareness".
When Haiku 5.5 came out though, for my tasks, the cost differences disappeared almost immediately. Haiku was good enough / fast enough for so many things that introducing a decider layer didn't offer huge wins. Jev's "millisecond" level performance was nice but would get dwarfed by other latencies in my chain ( web browsing ), that it didn't make a measurable difference in my tasks.
Why give all the context to an agent, when the context can also be curated, updated and made concise automatically for each new problem.
Not really a good analogy, but it feels like having an image you like but every time you view it, it runs it through another loop of jpg compression. Use it when it's most effective, otherwise let it preserve it's details.
They propose:
Valentino → Female Child
Yet this is obviously a boy name, else it would be Valentina. Is this model going to return confidence values? No, they want decisions. This is an inherent problem in AI with reductive logic for these tasks.
Likewise, dates. Is 17/08/22:
- 20170822 (Chinese write the year first, and in speech and chat they will say '17' not '2017'.)
- 20220817 (Everywhere But North America.)
Is 04/06/2009:
- 20090604 (Non-North American)
- 20090406 (American)
Without knowing the cultural context, the location, the document type, the document author, etc, you could be getting it wrong.
It’s an interesting problem. And i have following thoughts to it?
- Are all cases even decidable?
- Shouldn’t one false positive rank higher than a hundred rank negative.
-Shouldnt’t the AI just abstain from decisions to avoid false positives.
- Is it smarter to seperate this in a lot of small sub problems and to define this as a rule set of small yes no decicions, why does this need to be solved agentically?
- You should definitely clean up deterministically first.
I feel like if you really want to solve this problem, you would approach it differently. This seems like a wrong approach and a bad article on top?
No, and I think undecideable is quite frequent.
> -Shouldnt’t the AI just abstain from decisions to avoid false positives.
Yes, and I think regulation should be quite strong in this regard.
> - Is it smarter to seperate this in a lot of small sub problems and to define this as a rule set of small yes no decicions, why does this need to be solved agentically?
For larger contexts I worry about agentic confabulation, but humans unquestionably look at the gestalt when doing matching. I don't want idiot clankers making enforced decisions because they have misunderstood one field.
> - You should definitely clean up deterministically first.
If you can't do field validation on entry then I'm not sure deterministic rules work better, they may be worse, although easier to reason about.
Cf Lies we believe about Names, Addresses and Dates.
The system doesn't return confidence values because that's not what they are designing. The models, however, do and to me it's interesting that Jev's measure of confidence is pretty well calibrated to its correctness.
>Without knowing the cultural context, the location, the document type, the document author, etc, you could be getting it wrong.
And? There's no perfect process, just ones that asymptotically approach correctness at exponentially increasing cost. They own the system and get to decide what works for them.
Rather than binary classification we should be locating document fragments in a high dimensional feature space, and with all those fragments coming to a confidence bounded guess.
Additionally, when someone submits documents that can't be recognised as valid, the response should not be INVALID, it should be UNDECIDED. In that sense, passing to Opus is great, but passing a tiny fragment of the problem is a bad way to do it.
I don't usually get excited about the high-level, "whoooa man" marketing-speak, but the parallel is striking.
Some problems are obviously hard known-unknowns (eg “solve Navier Stokes”). The problem is unknown-unknowns or knowledge gaps, where the model doesn’t know they need help.
I don't believe the answer is ever "how certain is the model?" It's "how much is at stake?" Or "how hard of a problem does this appear to be?" It's not about teaching the model to have uncertainty, but rather to be avoidant of things that are sufficiently important to get right.
That isn't obvious, unless you've been doing ML for a while.
It's what I use locally. For my workload, it was right up there with Jev.
If you say 4x lower, that changes the semantics for lower than x vs higher than x, which is absurd.
GPT-6 Luna is a small LLM, not a decision model :P
Similarly, Grok has been routing to Opus internally for a little while now I think.
Any source on that?
Update: let me elaborate more, I read the HN entry about the team of 5 Qwens being close to fable quality... so I embarked to add to my harness (https://github.com/alainnothere/privibe because we all have one right?) and once I had added the orchestrate tool I had to now measure somehow it it's able to improve quality, so I claude and then Qwen to run a experiment to call my harness passing the model to use and ask them to do something, 3 rounds I think and see what quality came out of it... and as per the grading... qwen and claude agreed that the quality of the response comming out the harness using the orchestrate was VERY CLOSE, with opus and fable providing more "in my opinion you should" which matches my experience on why they are better.
One more update: I can't get copilot models to work decently, it may be my way of working... but I can get Qwen to follow what I try to do... if Qwen get's me he can then fire copilot cli and get amazing results by using copilot... bonus points seems like copilot cli is cheaper with the AIC (cost of the requests) so you can make the copilot subscription longer
Smart agent to orchestrate cheaper agents for labour.
Dumber agents to be very explicit about asking smarter agents for requests that require deep information.
This is a thing - the SOTA models really light up the more information you can provide them, and humans are too lazy / incapable of doing that in every prompt.
Entirely depends on what you are trying to do.
Edit: you can also use a smart agent to ask another smart agent, obviously, which is good for personal workflows.
What's the best method to have a 'smart' agent just.. ask itslef?
At this point if I could do anything with zero effort or time, I would get Opus 5.5 to make a grand scheme, handoff to the local model, and give it ability to reach out to Opus again.
That way you can do old school intent matching and rule-based bots that work super well, and only shell out for deeper work.
Claude will write some scripts to call the LLM API and use it like any other tool.
I haven't found a pressing need for anything more complex than that. My multi-agent setups tend to be a pile of bespoke scripts per project anyway, an MCP server is only gonna get in the way. But I suppose this is because I self-host the models, if you're using OpenRouter or whatever it may need more complexity than a curl call to an unauthenticated port
So with the gdrive app for my OS it just looks like a local file path. Had the local agent learn to parse it.
I still prompt in AI studio direct as needed; local agents see the log change but cannot push back to Gemini their result.
Few reasons:
AI Studio is available where ever via browser, no VS Code or my usual vim setup to miss. I can switch between projects there as an idea hits and the agent switches up the local repo path and session history.
AI studio can search web, etc, etc already so my agent is less complicated with addons in general
Also using AI Studio between local agent turns has been my "human in the middle" approach; to have an explicit gap in the process and check each turn.
I only have a 24GB 3090 most models also take longer locally and I never hit the free tier limits with Gemini :D
Though the latest Meta model is twice as fast at 30B for me as other similar size models. 1-2 hour code gen and QA of build errors is now 30-50 minutes to parse latest changes from Gemini and get a successful build. The Meta model is pretty great at "fill in the middle" from a well structured plan.
Overall this loop feels like a normal workday pace. I freelance so I have a lot more say over that.
https://tysonberry-wogan.com/command-and-control-by-tyson/
Ps: as you won't use your proxy to distill or alter claudecode requests to anthropic you should be compliant with anthropic rules.
Both are, I think, valid options. Depends on the workflow you're looking for.
https://opencode.ai/docs/agents/
There are a lot of ways to do this if you stop using Big Ai harnesses that make it difficult to use models besides theirs.
How is this different than just telling the model you’re using to fill in the gaps itself?
If you’re not manually reviewing the intermediate hop, how does that add to the “understanding you”?
Why Qwen directing the others? For the things claude refuse, Qwen can make very specific questions and still get an answer without triggering the thing.
It's like, find the relevant scientific papers by using Google search and the scientific paper MCPs, summarizing stuff, etc. Then the big model can come in and actually think on the artifacts
I ask task x -> big model thinks and delegate to luna heavy dumb work.
My sense so far is it’s very much about problem domain “taste” and there is no one correct answer (that is token cost efficient).
Does the smartest agent need the full flavor of the context of the problem to make real progress (i.e what’s been tried, how you’ve influenced the approach) or does the smartest agent really need a crisp problem definition and context room to really work.
https://code.claude.com/docs/en/advisor
It allegedly will automatically judge when to call the stronger model, not sure how much instructions in prompts or AGENTS.md can steer it.