Eight panels, one brine-soaked question, zero hand-waving: the tokenizer is named, the IDs are genuine, and the parts we invented are labeled.
Everything below is these nineteen words in motion. If a panel loses you, the term is up here.
A human types 23 characters of English. The model cannot read English — or characters. First stop: the slicer.
Full disclosure — the tokenizer: OpenAI’s GPT-2 byte-level BPE (the open,
inspectable gpt2 encoding in tiktoken), vocabulary
50,257 entries. We ran it for real. The 23 characters become exactly 8 tokens
(a leading · marks “starts with a space”):
Why5195 ·are389 ·pick2298 les829 ·w266 igg6950 ly306 ?30
Each ID is a row number in the model’s embedding table: ID 2298 fetches row 2298 — a learned vector of ~4,096 numbers (in a modern open-source model; 768 in original GPT-2). Position info is mixed in so “pickles wiggle” ≠ “wiggle pickles”. The 8 IDs are now an 8 × 4096 grid of numbers. Nothing else enters the model. (Vector values shown are illustrative; the lookup mechanism is exact.)
Full disclosure — the model: a decoder-only transformer (the architecture of GPT-2 and of today’s open-source flagships — Llama-class dense models and DeepSeek-class MoE). It does exactly one job: predict a score for the next token. Each block does two moves — attention (each token’s vector looks at all previous tokens’ vectors and blends in what’s relevant: “igg” discovers it belongs to w-igg-ly, which leans on pick-les) and an MLP (a per-token transform where stored knowledge — cucumbers, brine, turgor — gets stirred in). After the last block, the final position’s vector is multiplied against the vocabulary table one last time, producing 50,257 scores (logits): one per possible next token.
Generation is a loop: score all 50,257 candidates → softmax into probabilities → sample one → append its ID to the state → run the tank again. The token state grows by one each lap; a KV cache keeps the old jars so each lap only computes the newest token. (The candidate words & probabilities below are illustrative; every (token, ID) pair is a genuine gpt2 vocab entry we verified.)
| lap | token state going in (IDs) | top candidates (illustrative p) | sampled |
|---|---|---|---|
| 1 | [5195 … 30] (the 8 prompt IDs) | ·Pick 12346 .41 · ·Because 4362 .22 · ·They 1119 .11 | ·Pick (12346) |
| 2 | [5195 … 30, 12346] | les 829 .93 · le 293 .03 | les (829) |
| 3 | [5195 … 30, 12346, 829] | ·w 266 .55 · ·are 389 .18 · ·stay .09 | ·w (266) |
| ⋮ | …·iggle (24082), ·because (780), ·they (484), ·are (389), ·mostly (4632), ·water (1660), . (13)… | ||
Loop ends when the model samples an end-of-text token (gpt2 ID 50256) or hits a length cap. Final answer state:
Is it the same tokenizer? Yes — necessarily. The IDs only mean anything relative to one vocabulary table, so decode uses the same gpt2 vocabulary, run in the easy direction: ID → stored bytes → UTF-8 text, then concatenate. Encoding needs the clever merge rules; decoding is a pure table lookup. 12346→“·Pick”, 829→“les”, … glued into “Pickles wiggle because they are mostly water.” Same jar, lid on, lid off.
Where the agent actually lives. Not on a new chip — it is a control loop that runs on the CPU and rents the GPU by the lap:
| part of the agent | lives on | share of wall-clock |
|---|---|---|
| assemble context, parse the tool call, permissions, the loop | CPU | small, continuous |
| forward passes (panels 3–5) | GPU — often someone else’s, over the network | modest |
| running the tool (search, python, tests) | CPU + I/O | usually dominant |
| waiting | nothing at all | the biggest block |
| its “memory” | context — text, not silicon | — |
And the honest verdict for this question: the agent bought nothing. The model already knew pickles are mostly water — the loop cost ~3× the tokens and ~4× the wall-clock to reach the same sentence. Agents earn their keep only when the answer requires doing something — checking a live source, running code, editing a file, verifying against a test. Same discipline as the router in panel 5: don’t escalate when one forward pass suffices.
Yes — the choices retune the model’s weights (they’re some of the best signals we have):
① Confidence mining. Every sampled token carries its probability. Long low-confidence stretches (lap 3’s 0.55) flag exactly where the model is unsure — harvest those prompts as fine-tuning data.
② Preference pairs. The sampled path vs the runner-up path (“·Pick…” vs “·Because…”) is precisely the chosen/rejected pair that DPO / RLHF trains on: humans rank the two answers, and the gradient pushes probability toward the preferred token choices.
③ Distillation. The full 50,257-way distribution at each lap (not just the winner) is a teacher signal — a student model trained to match it inherits the behavior cheaply.
But not the tokenizer itself — with a caveat. Seeing pick+les and w+igg+ly repeatedly is evidence a bigger vocab could slice cheaper (fewer tokens = fewer laps = lower cost). That, however, changes every row-number in the embedding table, so you can’t “patch it in”: tokenizer changes mean retraining the embedding layer (or the model). In practice: tune weights freely from token signals; treat the tokenizer as frozen until the next big (re)train.
An agent is not a new kind of model — it is the same brine tank inside a plain while-loop. Each lap: the harness pastes together rules + tool catalog + history (all text), the same slicer tokenizes it, the tank emits tokens that merely describe a tool call, and the harness — ordinary non-AI code — actually runs it and pastes the result back in. The model proposes; the harness disposes.
Can: anything its tools reach — run code, read the failure, fix, rerun (self-correction against ground truth). Cannot: execute anything itself, remember beyond the re-fed context, learn mid-session (weights frozen), or verify itself without an external check — its confidence is a token probability, not a proof.