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Zero‑Context Architecture

The simplest path to reliable agents

Zero‑Context Architecture is the simplest and most reliable way to build agentic systems: isolate the domain completely, remove all history, collapse ambiguity, and present only the minimal solvable representation to the operator.

This section formalizes why 0‑context dramatically outperforms long‑context pipelines, why it eliminates most failure modes of LLM-driven agents, and how it achieves reproducibility at scale.

1. What Zero‑Context Actually Means

Zero‑context does not mean "no information."
It means:

  • no irrelevant information,
  • no accumulated history,
  • no cross-domain residue,
  • no uncontrolled entropy.

Formally, for state xₜ:

zₜ = Pₛ(xₜ)

Where Pₛ removes everything except the minimal slice required for the next operator.

The LLM never sees anything outside its current domain.

2. Why Zero‑Context Systems Are Stable

The operator sees only:

  • one domain,
  • one schema,
  • one goal,
  • one representation.

This drastically shrinks the manifold region:

latent_space(zₜ) << latent_space(full_context)

Smaller region → fewer attractors → predictable trajectories.

3. Why long context destroys reliability

Long-context systems:

  • re-encode the entire history every step,
  • let noise re-enter the latent space,
  • mix multiple domains at once,
  • introduce contradictory signals,
  • allow attractor collisions.

This causes:

  • drift,
  • hallucination,
  • inconsistent planning,
  • loops,
  • irreversible errors.

Zero‑context deletes the instability at the source.

4. Real Example: Browser Automation

Long-context version (what most agents do)

Prompt includes:

  • previous actions,
  • old DOMs,
  • chain-of-thought,
  • user instructions,
  • environment history.

The operator collapses all of this into a single unstable latent state.

Zero‑context version

zₜ = extract_active_DOM_slice(state)
rₜ = canonicalize(zₜ)
actions = f(rₜ)

Every step is:

  • fresh,
  • untainted by history,
  • strictly domain-limited.

The agent becomes reproducible.

5. Real Example: Document Extraction

Long-context agent

"Here is the whole PDF. Extract fields. Also here is what we did before."

This mixes:

  • layout noise,
  • prior partial extractions,
  • learned templates.

Zero‑context agent

section = extract_section(document, target_field)
rₜ = canonicalize(section)
fields = f(rₜ)

The model always sees the same representation for the same task.

This makes the workflow testable and deterministic.

6. Real Example: HR / Payroll Workflows

Long-context:

  • full employee profile,
  • unrelated attributes,
  • previous steps,
  • optional fields.

Zero‑context:

zₜ = jurisdiction_relevant_subset(employee)
rₜ = schema_align(zₜ)
steps = f(rₜ)

Only the domain required for the current compliance rule is visible.

7. Why Zero‑Context Enables Verification

Verification requires:

  • stable domains,
  • known structures,
  • predictable transitions.

Long-context destroys all three.
Zero‑context guarantees them.

Verifier design becomes trivial:

V(uₜ) = schema_check(uₜ) ∧ invariant_check(uₜ)

Because the operator can only act on canonical forms, the verifier is deterministic.

8. Zero‑Context as a Control System

Zero‑context is equivalent to a classical control system with perfect measurement isolation.

It aligns with:

  • state projection,
  • stable operator application,
  • closed-loop correction,
  • reproducible transitions.

It removes the uncontrolled internal memory that destroys multi-step reliability.

9. Why Zero‑Context Outperforms Every Open-Context Agent Design

1. No latent drift

Each step is independent.

2. No error accumulation

History cannot corrupt future steps.

3. No mixed domains

The operator sees only one domain at a time.

4. Reproducibility

Same input slice → same output.

5. Testability

Each step is a pure function.

6. Composability

Operators can be chained like Lego blocks.

7. Enterprise-grade determinism

Perfect for payroll, compliance, operations, identity workflows, and browser tasks.

10. How to Build a Zero‑Context Step

Every step follows this pipeline:

1. Project:     zₜ = Pₛ(xₜ)
2. Rewrite:     rₜ = R(zₜ)
3. Operate:     uₜ = f(rₜ)
4. Verify:      yₜ = V(uₜ)
5. Integrate:   xₜ₊₁ = I(xₜ, yₜ)

Nothing persists except the validated state.

11. Research Directions

  • optimal domain-projection algorithms,
  • canonicalization spaces,
  • formal stability under zero‑context constraints,
  • maximizing operator reliability via minimal representations,
  • quantifying entropy reduction from strict isolation.

Zero‑Context Architecture is the reason your agentic systems achieve reliability where others collapse. It is the structural backbone of verifiable automation.

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