AI agents enter the territory code can't write — long-tail × tacit knowledge × tacit thoughts
Automation has always stopped at explicit knowledge. RPA, SaaS, core systems — they all only swallow work that can be flowcharted. AI agents are the first to descend into the tacit, and into the layer just before that — what I'm calling tacit thoughts: the situation-aware territory.
Connecting Nikkei xTech's April 2026 "long-tail × AI" piece with Polanyi's tacit knowledge, Nonaka's SECI, Karpathy's LLM Wiki, and "tacit thoughts" (a coinage) into one layered model.
The frontier of automation has moved from explicit knowledge to situation-aware work. Only LLM agents can descend into the tacit and sub-tacit — which is why long-tail processes finally crack open and why individual tacit thoughts become usable for the first time.
Compiled from two Vault notes: 5/17 "Long-tail processes × AI agents" and 5/18 "Obsidian × Hermes — tacit thoughts as your own twin." A team of one or two at a corporation and a single person's head share the same structural problem (the tacit layer dwarfs the rest), and both become tractable only with AI agents.
- A 3-layer model: explicit → tacit → tacit thoughts. The deeper you go, the more there is — and nothing could touch it until now.
- Long-tail processes (1–few owners, varied, person-dependent) resisted automation for so long because they are essentially tacit-knowledge sinks.
- The real strength of AI agents = "territory code can't write" = situation-responsive work. They descend into a layer deterministic RPA / SaaS never reached.
- For individuals: pile tacit thoughts roughly into Obsidian × an AI agent reads them as your twin. For enterprises: long-tail DX gets a step-change in resolution.
Long-tail processes — the seam of 1–few owners
Large enterprises follow a power-law distribution of work. The head — hundreds to thousands of people doing the same thing (expenses, ordering, HR requests) — has been covered by core systems and RPA for years. The tail, on the other hand, hosts vast variety performed by only one or two people each. In April 2026, Nikkei xTech named this "long-tail processes."
Examples Nikkei lists:
- Project-specific budget approvals (conditional, custom routes)
- Multi-stage approvals for rare procurement or unusual travel
- Department-specific expense rules
- Complex settlements for R&D and grant funding
- Niche vendor onboarding
- Department-specific Excel aggregations and reports
- Low-frequency parts ordering, niche legal review
The common pattern is clear: "not a process with hundreds of people," "flow differs every time by team/case," "the rules live only in the operator's head." So RPA / SaaS / core systems can't justify the ROI.
Long-tail processes = 1–few owners × highly varied × person-dependent. They were dropped from systemisation because they were "important but impossible to optimise as one thing" — the last automation frontier inside the enterprise.
The tacit-knowledge wall
The reason long-tail processes resisted automation isn't low volume. It's that their substance is almost entirely tacit knowledge. That's the real wall.
>2-1Polanyi's tacit knowledge
The philosopher Michael Polanyi (1958) gave us "We know more than we can tell." People know beyond what they can verbalise. Riding a bike, recognising faces, an expert craftsperson sensing "the curvature is off" — none of these are fully explainable by the person doing them. Polanyi called this tacit knowledge.
>2-2Nonaka's SECI and "externalisation"
Ikujiro Nonaka's SECI model (1995) frames organisational knowledge creation as a loop: Socialisation → Externalisation → Combination → Internalisation. The hardest step is Externalisation — turning individual tacit knowledge into words/diagrams others can use. This is exactly where long-tail automation gets stuck.
>2-3Long-tail = a tacit-knowledge sink
The fewer the operators, the more person-dependent the work becomes. The more person-dependent, the more judgement criteria, exception handling, "last time we did it this way so this time too" local rules pile up as tacit knowledge inside one head. Nikkei calls this out as the biggest wall:
Interviews with operators are needed to verbalise tacit knowledge. If extraction is shallow, the AI behaves incorrectly and can't handle exceptions.
In other words: the long-tail layer is sunk beneath a tacit-knowledge ocean. To put it in code at all, you have to surface the tacit first.
A new layer in front of that: "tacit thoughts"
There's one layer below this. The layer just before tacit knowledge. I'll call it tacit thoughts (a coinage).
>3-1Definition
- Tacit knowledge = hard-to-verbalise experience / judgement criteria (an expert's accumulated know-how)
- Tacit thoughts = not yet knowledge — the wobbles, hunches, hypotheses, indecision, judgement habits, directions of curiosity, recurring themes in your head
Tacit knowledge is "judgement that has solidified inside an expert, even if they can't explain it." Tacit thoughts are "raw material before it solidifies." In SECI terms, this is the still-circulating cognitive layer inside one person, sitting before tacit knowledge.
>3-2Examples
- "Something about this proposal nags at me" — a wobble
- "I sense these two things connect" — a hypothesis
- "Between A and B, I can't decide yet" — indecision
- "I always lean toward context over numbers" — a judgement habit
- "Lately my mind keeps returning to X" — a direction of curiosity
If tacit knowledge is "knowledge before it can be told," tacit thoughts are "thinking before it becomes knowledge." They sit upstream of Polanyi, are more fragile, and exist in vastly greater volume. Everyone generates them daily — yet until now, there was nowhere to record them and no one to use them.
The layered model — the order AI descends through
Stack the three, and you get a layered model of knowledge:
- Explicit (above water) — manuals / SOPs / work already in code
- Tacit (below water) — an expert's judgement, knack, exception handling, "now that you mention it, yes"
- Tacit thoughts (sediment) — wobbles, hypotheses, indecision, habits, directions
Automation history has descended through this stack:
- ERP / core systems (1990s→) — only the most well-defined high-volume routine in the explicit layer
- RPA (2010s→) — descended into still-manual explicit work
- SaaS (2010s→) — templatised cross-industry explicit knowledge
- LLM agents (2023→) — the first technology to break the waterline, reaching tacit, then tacit thoughts
Why only AI agents can descend into the tacit
The crux is simple: code is deterministic, agents are situation-responsive.
>5-1Code = deterministic
RPA, SaaS, classical scripts — all the same principle. You write "if A then X" and "if B then Y" ahead of time. Inside the branch net you wrote, it runs perfectly. Step outside the net, and it breaks. Long-tail processes are "outside the net" by nature, so deterministic approaches are structurally wrong for them.
>5-2Agents = situation-responsive
LLM-based agents work differently. Every time, they read the situation, judge, and act. Same task, different input → a different route. If something exceptional comes in, the agent doesn't force a fit — it asks for more info. Even with no formalised judgement criteria, it can infer from natural-language context and still act. This is the property we call "situation awareness."
>5-3Tacit and sub-tacit are "language work"
Tacit knowledge and tacit thoughts can't be captured by code, but they can be expressed as nuance in natural language. LLMs have the rare property of handling linguistic ambiguity directly. From incomplete descriptions like "in cases like this I usually do X" or "something feels off" or "probably this one," an agent can compose a situation-aware response.
The frontier of automation has moved from "making explicit knowledge efficient" to "taking over situational improvisation." The territory code can't write is exactly where AI agents are at their best.
A map of agents already shipping — present tense, not future tense
To keep this from staying abstract, here are working systems as of May 2026. Map them on two axes — individual / business × tacit / sub-tacit — and all four quadrants already have real agents.
>6-1Business × tacit — Daikin / Misumi
- Misumi's "technical-support AI": reproduces over a decade of veteran engineer judgement on customer drawings and requirements at around 90% accuracy. Demonstrates that what could "only be asked of a human" can be substituted by an agent.
- Daikin Industries' "shop-floor tacit-knowledge AI": encodes the quality judgement and tuning know-how of factory veterans. Won the top prize plus the AI Agent prize at the 2026 NEDO project. Already positioned as a pillar against tacit-knowledge loss through retirement.
Both exemplify "business-specific × externalisation of tacit knowledge." The "interview operators to surface tacit knowledge" step Nikkei pointed to is being performed continuously by AI embedded inside the business.
>6-2Individual × tacit — Claude Code / Cursor
- Claude Code / Codex: substitutes for the individual engineer's intuitive judgement across a codebase. "If this is the repo, this is how I'd write it" — that person-dependent judgement (tacit knowledge) is what the AI has descended into.
- Cursor: an in-editor AI that progressively substitutes the writer's contextual intuition. The most everyday example of tacit-knowledge substitution in coding work.
>6-3Individual × sub-tacit — Hermes Agent / ChatGPT・Claude
- Hermes Agent (OSS): a twin that reads the tacit thoughts themselves piled in an Obsidian Vault. Details in "Hermes Agent — execution engine for your Second Brain". The reference implementation for the setup covered in §07 PRACTICE.
- ChatGPT / Claude.ai: in single-shot dialogue, functions as the place that surfaces wobbles you can't articulate yet. The most widely-used form of tacit-thought externalisation is "throwing in-progress thoughts at a partner."
>6-4Business × sub-tacit — Jitera
- Jitera: positions itself on its official site as "turns your code, docs, decisions, and tribal knowledge into living context," and explicitly states it is "looking one step ahead — at the tacit knowledge that lives between the lines… including the things that rarely get written down." This is precisely the tacit-thoughts territory of this post, taken on directly as a business-side AI-agent platform. Beyond code, docs, and decisions, the differentiator is feeding "the unwritten details that shape how a company actually moves" into AI as living context.
Compared with Daikin / Misumi, which AI-ify Polanyi-style tacit knowledge, Jitera steps directly into the phase that is "not yet written, not yet formed" — making it the representative example of the business × sub-tacit quadrant.
In 2023, every quadrant was "future talk." As of May 2026, every quadrant has a commercial or OSS example shipped. The tacit territory has moved from debate-and-hypothesis to the front line of implementation competition.
Personal practice — Obsidian × AI as your twin
At the individual level, the pattern is pile tacit thoughts roughly into Obsidian × an AI agent reads them as your twin. The intellectual foundation of the setup in the Hermes Agent piece sits here.
>7-1The value of "not organising"
Tacit thoughts die the moment you organise them. Tagging, formatting, headings — all of these strip the raw material before it solidifies. So the right move for daily/ is leave it rough. Wobbles, contradictions, half-formed hypotheses — keep them all. Demand polish, and the will to input dies entirely.
>7-2Two-layer setup: daily / knowledge
daily/<YYYY>/<YYYYMMDD>/(the raw layer) = the tacit-thoughts dump. AI doesn't touch it (suggestions only; never rewrite).knowledge/(the hub layer) = INDEX + theme-specific MOCs. AI grows this one. Reads tacit thoughts in daily/ and proposes / appends related clusters.
The structure of Karpathy's LLM Wiki, applied directly. The division is clear: human = the device that generates tacit thoughts; AI = the device that organises and uses them.
>7-3The role of a resident agent (Hermes)
A resident agent (e.g. Hermes) reads the Vault daily and lifts "what's usable now" from tacit thoughts and recomposes it. Ping it from your phone with "summarise that thing" — Hermes reads your Vault's tacit thoughts and answers. That's the moment tacit thoughts first become "things you can call up and use."
Once this division works, AI stops being a single-shot chat partner and becomes a continuous partner with your own context. An AI that can read your tacit thoughts knows your judgement, habits, and curiosity — enough to be worth calling your twin.
Long-tail DX — from interview support to tacit-thoughts collection
The same model maps directly onto enterprise DX. The Nikkei piece's "AI-assisted tacit-knowledge interviews" get even sharper when you descend further to the tacit-thoughts layer.
>8-1Old DX: harvest the head only
RPA / SaaS / core-system rollouts only harvested head-side hundreds-of-people processes and stopped there. The tail was left alone because the ROI didn't pencil. On the ground, this kept producing the same complaint: "we rolled it out and it still isn't easier."
>8-2Surfacing tacit knowledge (short term)
Stage one is LLM-assisted tacit-knowledge interviews. The operator and an AI talk back-and-forth, surfacing judgement criteria, exception handling, and knack. AI takes over the externalisation step that Nikkei called "the biggest wall." In SECI terms: accelerating Externalisation.
>8-3Collecting tacit thoughts (mid term)
Go one step further, and the operator's wobbles, hunches, and hypotheses — i.e. tacit thoughts — become collectable too. Until now, there was nowhere to record this as "business know-how." In the agent era, it makes sense to have an "operator tacit-thoughts pool" in an Obsidian-like place, with an AI using it. Only then do quality of exception handling and quality of handover finally lift.
>8-4Delegated execution by resident agents (long term)
Once tacit and tacit-thought layers accumulate on the AI side, processes the operator no longer has to run personally start expanding. Unlike RPA, agents judge by situation each time, so they can absorb the "slightly different every time" nature of the long tail. This step can overtake a decade of deterministic-code accumulation at once.
The value of Applied Engineer / FDE roles (covered in a separate post) ties in directly here. Going down from the field's tacit knowledge into tacit thoughts and pulling verbalisation out of people is something neither an upstream consultant nor a pure engineer can do alone. "The person who runs all three layers end-to-end" is the lead role of long-tail × AI-era DX.
The territory code can't write is the agent's main stage
Automation has descended layer by layer: explicit → tacit → tacit thoughts. Deterministic code (RPA / SaaS) can't climb past the head's explicit-knowledge ceiling. Only LLM agents act situationally, which is why they descend into tacit and sub-tacit.
At the individual level, the pattern is pile tacit thoughts in Obsidian; an AI twin reads and uses them. At the enterprise level, long-tail processes finally become automatable. The same structural problem is being solved on both fronts at once.
- Humans = devices that generate tacit thoughts (rough, ungroomed, in volume)
- AI = the device that reads and uses them (recomposed by context every time)
- That division cracks open the long tail and grows a personal twin
From "automate it in code" to "hand the uncodifiable to AI." Not a continuation of efficiency work — a qualitative change in what automation can touch. Long-tail processes opening up and personal twins becoming real share the same root.