Ronald Wacker

Architecture

The architecture contract: ten rules every feature obeys.

1. 01

   Event-first. Every input becomes an immutable record before anything updates.

2. 02

   Two memories. Structured queryable facts alongside a narrative summary per person. Raw logs are never shown without a synthesized summary.

3. 03

   Signals to Decisions to Actions. No recommendation may exist without the stored signal that produced it.

4. 04

   AI holds fixed roles and never replaces data.

5. 05

   Every inferred fact, task and score carries confidence, evidence, and a one-sentence explanation.

6. 06

   BBL is the truth for property. Not address, not BIN.

7. 07

   A feature is not done unless it flows Event to Memory to Signal to Recommendation to one-tap Action.

8. 08

   Ambiguous matches require human approval.

9. 09

   Push, do not pull. The system initiates.

10. 10

    Outcomes, dismissals and corrections are stored as feedback, not discarded.

The intelligence pipeline

The model touches the two ends, never the middle.

Data boundaries

Three concentric data rings, from raw public staging inward to private user-owned records.

Decisions

Scoring uses no language model

Deterministic and auditable. Intent, urgency, engagement, quality and recency each return their own score and reasons, persisted per dimension. A score that cannot be explained in one sentence violates the explainability law. Recency can go negative: silence actively decays a lead.

Pressure stacking over single signals

Signals bucket into financial, family, work, housing, active search, property-specific and behavioral, with weights. A transition moment fires only when two or more categories stack, because stacked pressure is qualitatively different from any single signal.

Person ID is nullable on the event log

An unknown inbound call still becomes a record, flagged for review, and lands in an unassigned inbox. Data is never dropped for lack of identity.

Ambiguous matches go to a human queue

Name similarity plus address corroboration on an explicit confidence ladder: high confidence auto-links, the middle band routes to an approval queue, low confidence is discarded. False positives corrupt memory permanently; missing links are cheap to fix.

Task rules as data, not branching code

An ordered table of condition, action, reason, priority, timing and channel. Adding a trigger is one entry, and every task ships with the sentence explaining why it exists.

A deterministic formatter overrides the model

Building facts bypass the model's prose entirely and render through tested formatting code. Those facts are too important to let a model paraphrase.

Source priority baked into tool names

The model sees primary source, official records and supplemental context as labels rather than as a policy it might ignore.

Detectors written ahead of their data

Opportunity detection probes which data sources actually have rows, then runs only those detectors. Detectors for future sources are already written and lie dormant.

Stack

Result: a working system with a specification of roughly 3,400 lines behind it, built solo in about one week.

Architecture

The skills library as a map, grouped by the function each one removes, with trigger and output for each. Research passes chain into specification, specification into support coverage, and every output inherits the voice layer.

Decisions

Skills, not prompts

Codified procedures as reusable skills rather than one-off prompts. Repeatability over cleverness.

Research separated from composition

A research pass can be verified before anything is written from it.

Voice as its own layer

Voice and brand rules live in one place, so every output inherits them instead of restating them.

A chained run

Adding a visa pathway to the platform has automated steps and manual ones. This is not end to end.

The chained portion runs pathway research into a verified fact sheet, the fact sheet into a specification, and the specification into requirement lists and knowledge-base articles. Each output feeds the next.

That chain produced the preparation packages for the Greece and Italy retirement pathways, the two the Impossible To Name section lists as in assessment. The chained preparation ran in roughly half the time the same work took by hand for the first pathway. The build itself stays manual.

Architecture

Agent topology with isolation boundaries drawn explicitly. Model tiering is part of the design: composition and research on the larger model, both gates on the small one.

The two-gate flow

The truth gate runs first; the effectiveness gate only ever sees text that has already passed it.

Reverse-engineering the screen

The screening tier is a black box to candidates, but it is not an unknowable one. The reconstruction is built from Ashby's published rules and audit data: how criteria are generated from the posting text, the rules for writing them, and an audit reporting that 11.4 percent of real evaluations land on UNCERTAIN. Ashby's credit economics, where one credit covers one candidate against up to fifty criteria, imply the screening tier runs on small, efficient models. The pipeline reconstructs that screen three ways and scores against all three, because a single reconstruction would be a guess presented as a number.

The reconstruction is what makes the rest of the system possible: the evaluator is pinned to a small model precisely because that is the tier the reconstruction says is doing the screening, and the three-valued verdict exists because that is what the screen returns.

Decisions

The evaluator must stay small

The decision most likely to be “fixed” by someone optimizing for quality. It is not trying to be a good judge. It is simulating the deployed screening tier, and the vendor's credit economics, one credit per candidate against up to fifty criteria, imply that tier runs on a small model. A larger model resolves ambiguity in the candidate's favor, performs inference a junior screener will not, and returns false passes. Upgrading it makes the numbers prettier and the signal worse.

Three-valued output

Meets, does not meet, and uncertain, because uncertain is the point. For a non-standard background the dominant failure mode is not rejection, it is a screener who cannot tell. So the test for every sentence is whether a literal reader could be unsure, never whether it is impressive.

Two gates, opposed incentives

The auditor pressures toward provable claims, the evaluator toward stronger ones. A line ships only if it satisfies both. Truth precedes effectiveness, because scoring an unshippable draft produces pressure to keep whatever earned the score.

A third state for untraceable claims

Traced or deleted is the naive design, and deletion silently destroys true but unrecorded facts. The third state routes the claim to the human who can rule on it.

Confirmed negatives outrank criteria

The dangerous claim is not the false one, it is the adjacent-true one that is ninety percent accurate and collapses under a reference check. A plain fact base cannot express “this is almost true and the almost is fatal.” Entries are dated rulings with explicit carve-outs, not rules.

Refusing to ship is a first-class output

If an application cannot be won honestly, the system says so and stops, and routes to a different channel instead. A skip is an instruction, not a judgment.

No agent without an opposed incentive

A constraint on the architecture itself: never add an agent unless isolation buys a genuinely opposed incentive. Each agent exists because it needs to not know something, or to want something different from the writer. Anything else is an instruction inside an existing prompt.

Nondeterminism is measured, not smoothed

Verdicts that move across runs on unchanged text are labeled unstable and must not be read as earned improvements. Instability clusters in the speculative gates, which depend on the model's outside knowledge rather than the text, so they are coin flips by construction. A pipeline reporting a clean deterministic number would be lying about the thing it simulates.

Stack

Result: every application produces a tailored résumé, audited form answers, and a verdict sheet carrying quoted evidence per criterion across three reconstructions of the screen, plus a routing decision.

Weaknesses

Isolation by discipline

Evaluator isolation is enforced by prompt discipline rather than by the tooling. Revoking the read grant would make it structural.

Honest stubs

One per-ATS playbook, Ashby, is fully researched. The others (Greenhouse, Workday, LinkedIn) are honest stubs that halt rather than improvise.

No ground truth

Criteria reconstruction is a model of what the screen generates, grounded in the vendor's published rules, with no ground truth available to score it against.

Small outcome data

Outcome data is small, and everything about calibration is provisional until more accumulates.

Architecture

00-canon/

Rates, framework, red flags, voice, profile. The decision-time reads.

00-canon/CONFLICTS.md

Inherited contradictions, left unresolved on purpose, for the operator to arbitrate.

.claude/agents/

Eight agent definitions, each pinned to a model.

deals/

One folder per inbound: raw.md, record.yaml, research.md, analysis.md, reply-draft.md.

contacts/

People, not deals. Survives an agency change.

views/

Generated: CONTACTS, PIPELINE, obligations, comps.csv, accounting.xlsx, and one list per deal type. Never hand-edited.

fixtures/

Known-outcome cases. Run after every canon edit.

scripts/hooks/

Floor gate and auto-rollup.

log/

decisions.jsonl, learnings.md.

Model routing

Eight agents, each pinned to a model in its own frontmatter. Cost is a design parameter here, not an afterthought.

A typical inbound fires four agents, not eight. Each subagent costs roughly 25–35k tokens just to initialize, so fanning out on every deal would erase the savings from routing the cheap work to Haiku.

Leave CLAUDE_CODE_SUBAGENT_MODEL unset. It overrides the per-agent frontmatter and silently collapses this whole table to one model.

Scheduling

0 8 * * 1  cd /path/to/repo && claude -p "Run archivist. Report only what needs attention." >> log/weekly.txt

Headless claude -p survives a reboot and does not need the desktop app open. Each run is a full session and counts against usage limits.

Decisions

Enforcement, not judgment. Every rule below is a check a script can run; none of them require an agent to have taste.

Every draft declares itself

A reply-draft.md opens with a machine-read declaration: <!-- QUOTE: none --> or <!-- QUOTE: <rate-card-key> <amount> -->. The gate validates the declaration against canon floors, checks that the declared amount actually appears in the body, and, when quoting, requires the usage-window and exclusivity asks. Declines that mention the other side's lowball number pass cleanly.

Floors live in one place

The frontmatter of 00-canon/rates.md, parsed at gate time. Unreadable canon fails closed for quotes.

Absence is a value

validate() flags a signed deal with no usage_window_end, because an absent end date defaults to perpetual.

Category collisions

exclusivity_check.py catches them before a quote goes out, not after.

Nothing sends

Nothing is marked paid by an agent either. Both irreversible actions stay with the operator.

Stack

Weaknesses

Canon contradicts itself

Seven inherited contradictions sit unresolved in CONFLICTS.md, parked there deliberately rather than papered over. Two are material: until they are arbitrated, every rate recommendation downstream of them rests on a default the system picked rather than a decision the operator made.

Gates enforce, they do not judge

The floor gate checks that a quote declares itself, matches canon, and appears in the body. Whether the number is right for this particular deal is not a thing a gate can check.