Learns on Your Machine

Suggestions in the OrderYou Actually Take Them

Plexon notices which agents finish the job and which of its suggestions you accept, then picks by it. Before it has watched enough to be right, it does nothing: with no evidence the pick is the one you get today, ties and all. What it learns is one file on your machine, shown to you with the run count behind every number, and one button puts it back.

So a task that did not name a specialist still lands on the right one. Naming an agent yourself always wins.

Day One Is Unchanged

Every ranking in Plexon has always come from a fixed rule that never finds out whether it was right. Adaptive routing gives that rule a way to be corrected, and it corrects it only in proportion to the evidence. With nothing observed yet, what Plexon learned counts for nothing and the order is the rule's order, ties included.

  • No observations means a learned weight of zero, so the ranking is the old one
  • Equal scores keep the order the catalog already gave them
  • Two observations move a ranking by about a third at most
  • Four observations before a learned score counts as much as the original rule
  • A test asserts the cold order 500 draws in a row, exactly

Signals Plexon Already Had

Nothing here invents a new thing to measure. Every signal was already produced and thrown away: a suggestion you took or waved off, the verdict the verifier agent filed, the status a loop agent reported, whether a workflow run finished. What you typed next is not one of them.

  • You accepted the suggestion, or you dismissed it
  • The verifier filed pass, fail, or unverifiable, which is worth half credit
  • A workflow run reached completed, or it errored
  • A cancelled run counts for nothing. You stopped it, which says nothing about the choice
  • Nothing reads "actually, do X instead" as a complaint about the last decision

The Verdict Usually Arrives Late

A verifier verdict lands after the turn that caused the decision, sometimes several turns after. That is the normal case, not the exception, so each decision is filed with the id the later signal will carry and waits for it. If nothing ever comes, nothing is assumed.

  • A verdict that arrives three turns later still reaches the decision that earned it
  • The same signal twice resolves once
  • A decision with no outcome after 7 days is dropped, and no reward is invented for it
  • An echoed template like "VERDICT: pass | fail" parses as nothing at all

Old Evidence Fades

A preference from last spring should not outrank what you did this week. Evidence decays once a day, keeping about half of itself over a month, and an option that fades all the way back to where it started is removed rather than left sitting in the report saying nothing.

  • Daily decay retains roughly half the evidence over a month
  • An option decayed back to its starting point disappears from the report
  • A long gap between sessions is caught up in one pass, not replayed forever
  • A rule that changes between releases cannot overwrite what was learned about it

You Can See What It Learned

Settings, Advanced, "What Plexon learned" prints the whole state: one block per context, one line per candidate, each with its score and the number of runs behind it. The run count sits next to the score on purpose. A 100% built on two runs and a 62% built on ninety look identical when you only read percentages, and only one of them is worth anything.

  • One block per context bucket, such as capability|code
  • Every score carries the count of runs it came from
  • Reset one bucket, or reset everything, from the same card
  • After a reset, behaviour is day-one behaviour again
  • The file path is printed, because deleting it by hand is a legitimate way out

It Picks a Specialist, and You Can Stop It

A sub-task that names no agent used to run generic. That was already a choice, just one nobody was watching, so picking the specialist that finished the job before is on by default. Bidding keeps its own switch, because it is the only part that spends anything.

  • Learned agent defaults are on, with a switch in Settings that turns them off
  • With nothing learned the pick is the plain description match, so day one is unchanged
  • An @agent mention or a manual pick is an instruction, and nothing re-ranks it
  • An agent whose declared modes exclude the current mode is never selectable
  • Bidding costs one short model call per candidate, at most four, and only for a close call

It Never Leaves the Machine

What Plexon learned about your choices is a profile of one person, which is why it is treated like a credential rather than a cache. It lives in a single JSON file at ~/.plexon/routing.json, written with owner-only permissions. No part of it is attached to a request: not a context, not an option, not a score.

  • One local file, written atomically, readable only by your account
  • Nothing from it is ever sent with a prompt
  • A corrupt file costs the learning, not the feature. Plexon starts cold and keeps working
  • Turn it off and every ranking is the fixed rule again, everywhere

Under the Hood

One Posterior per Option

Thompson sampling over Beta posteriors, one per option per context. An option with thin evidence has a wide posterior and occasionally wins on its own, so exploration comes free with no dial to set.

Four Runs to Parity

A learned score reaches the weight of the original rule at four observations. Conservative on purpose: the cost of learning slowly is an order that stays put, and the cost of learning fast is behaviour that swings on three runs.

A Seven-Day Window

A decision waiting on an outcome keeps waiting for a week. Past that the turn is long finished and no verdict is coming, so the entry is dropped rather than scored.

Reset Is the Undo

Per context or all at once. Behaviour that drifts over time with no way to put it back is indistinguishable from a bug, which is why the reset button came before the feature did.

Tool Search Stays Fixed

Finding the right tool is not up for learning. It is checked against a fixed corpus for top-one accuracy, and a tool that stops being found is a tool the AI cannot reach.

Per User, Per Machine

The file is yours alone. Nothing is pooled across accounts, nothing is compared against other people, and nothing trains anything.

Connects to

Nothing here works alone. See the whole map for how the pieces fit together.

What feeds it

  • 156 AI Agents feeds this.

    Which agents finish the job is the evidence routing learns from.

Where it goes

  • This feeds Multi-Agent Orchestrator.

    A subtask that names no agent goes to the specialist that finished that kind of work before.

  • This feeds Prompt Suggestions.

    Which suggestions you take reorders the ones you are offered next.