IBM Bob 2.0 Hackathon · lablab.ai · September 25–27, 2026

Tarsius Capture Knowledge That Was Never Written Down

A tacit-knowledge capture & persistence layer for IBM Bob 2.0. When senior engineers retire, they take undocumented business rules with them. Tarsius captures that knowledge before it's gone — and persists it so AI never forgets.

72% Over 50
RPG Developers Approaching Retirement
0–19% BER
AI Logic Loss without Safety Net (arXiv:2605.17535)
Zero Docs
Critical Rules That Were Never Written Down
18 Sources
Peer-Reviewed Papers & Global Surveys

The Knowledge Disappearance Crisis

Critical operational knowledge is vanishing from IBM i teams — and existing AI cannot recover what was never written down

Legacy Crisis

The Retirement Wave

72% of RPG developers are over 50 (Fortra 2026). Skills gap = #1 concern (69%), overtaking cybersecurity for the first time in 9 years (Fortra 2026). When they leave, they don't just take syntax knowledge — they take undocumented exceptions, workarounds, and gotchas that were never written anywhere.

Sources: Fortra IBM i Marketplace Survey 2026
Verification Crisis

The Written-Word Blind Spot

IBM Bob reads everything written: repository-wide context over source code, document understanding over specs. But all of it requires knowledge to be already written down. When a critical rule exists only in someone's head, even the best agent silently fails — generating plausible code that violates a rule no one remembered to state.

Sources: IBM Bob documentation, Tarsius analysis
Logic Loss Gap

The Knowledge Gap Is Permanent

Once a senior developer retires without a structured handoff, that knowledge gap becomes permanent and unrecoverable. No future AI tool, however capable, can extract knowledge from a document that was never written. 30-50% cost overruns are common when teams must reverse-engineer intent from code alone.

Sources: Gartner 2026, Industry analysis

End-to-End Knowledge Capture & Persistence Architecture

How Tarsius integrates with IBM Bob 2.0 to capture tacit knowledge and persist it as a binding contract that constrains all future code generation

SYSTEM DATA FLOW & AGENTIC ORCHESTRATION Interactive Visualization
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flowchart TD
    subgraph PHASE1["PHASE 1: Capture — Structured Interview (Legacy Analyzer)"]
        direction LR
        A[Legacy RPG Code + Specs] --> B[Subagent A: Rule Extractor]
        A --> C[Subagent B: Tacit Knowledge Interview]
        B -->|MCP: write_finding| D[(tarsius-bri.json)]
        C -->|Surfaces undocumented exceptions| D
        D --> E[Deterministic Risk Triage 🔴🟡🟢]
    end

    subgraph PHASE2["PHASE 2: Persist & Reinforce — Human Knowledge Gate"]
        direction LR
        E --> F[React Dashboard Visual Risk Map]
        F -->|Human Approve 🔴 & 🟡| G[MCP: mark_approved]
        G --> H[Auto-Generate RISK-CONTEXT.md + Decision History]
    end

    subgraph PHASE3["PHASE 3: Reinforce & Learn — Constrained Generation + Verification"]
        direction LR
        H -->|Lifecycle Hooks Re-inject Rules| I[Subagent C: Code Transformer]
        I --> J[Generated Code]
        J --> K[Subagent D: Confidence Verifier]
        K -->|MCP: verify_equivalence| L{BETH: BER?}
        L -->|< 100%: Deviation| M[Human Review or Auto-Remediate]
        M --> I
        L -->|100% — 12/12 vectors| N[Commit & Record Decision]
    end

    style PHASE1 fill:#0d1324,stroke:#2a3560,color:#e8edf5
    style PHASE2 fill:#0d1f1a,stroke:#34d399,color:#34d399
    style PHASE3 fill:#1a1424,stroke:#a78bfa,color:#a78bfa
          
Phase 1 · Capture

Structured Interview & Rule Extraction

Subagents scan RPG code and attached spec documents. The interview surfaces implicit rules Bob's static analysis cannot detect — undocumented exceptions, workarounds, and gotchas that exist only in a senior engineer's memory.

Phase 2 · Persist

Knowledge Persistence & Binding Contract

Approved rules are written to a versioned rule ledger (BRI) with immutable audit trail. Tarsius generates RISK-CONTEXT.md — a binding contract that constrains all future AI generation.

Phase 3 · Reinforce & Learn

Context Persistence & Verification

Lifecycle hooks re-inject approved rules before every code generation task — even across session boundaries. BETH catches deviations vector-by-vector. Gotcha memory accumulates.

4 Specialized Subagent Roles Native to IBM Bob 2.0

Capture, persist, reinforce, and learn — orchestrated via Bob's native subagent system

1
rule-extractor-agent

Rule Extractor (Capture)

Scans legacy RPG/COBOL code, extracts implicit & explicit rules, surfaces undocumented exceptions, and writes structured records into BRI JSON via write_finding().

2
contradiction-auditor

Tacit Knowledge Interviewer

Guides senior engineer through structured review, surfaces rules Bob cannot detect. Compares docQuote vs codeLocation to flag contradictions.

3
code-transformer

Code Transformer (Reinforce)

Generates code strictly constrained by approved rules in RISK-CONTEXT.md. Lifecycle hooks ensure rules persist across sessions.

4
beth-verifier

BETH Verifier (Verify)

Runs the BETH differential oracle: 12 ORDVAL vectors against the SME-approved rules. Deviations surface per vector. Decision history + gotcha memory accumulate for future sessions.

Knowledge Persistence Layer

How approved rules persist across sessions and constrain future AI generation via RISK-CONTEXT.md

JSON BRI Data Source tarsius-bri.json
{
  "id": "BR-DISC-EXCEPTION",
  "title": "Suspended accounts may submit DISC orders",
  "triage": "🔴 must-review",
  "approvalStatus": "approved",
  "contract": {
    "precondition": "status == 'SUSPENDED' && type == 'DISC'",
    "expectedBehavior": "ALLOW_ORDER",
    "forbiddenBehavior": "BLOCK_ORDER"
  }
}
Auto-Generated Binding Contract .bob/RISK-CONTEXT.md
### 🟢 Approved Rule: BR-DISC-EXCEPTION
- Description: Suspended accounts may submit DISC (CS-4471).
- Location: ORDVAL.rpgle (Lines 58-66)
- FORMAL INVARIANT CONTRACT:
  - WHEN: account.status == 'SUSPENDED' AND order.type == 'DISC'
  - MUST: Return ALLOW
  - NEVER: Return BLOCK

Interactive Demo Scenario: 2 Aha Moments

The 3-minute pitch flow proving contradiction detection and AST self-healing

Aha Moment #1 Phase 1 & 2

Tacit Knowledge Surfaces a Hidden Exception

The written spec states: "Suspended accounts cannot submit orders". But legacy RPG code has a 2010 legal exception (CS-4471) that was never documented. This rule existed only in a senior engineer's memory.

🔴 BR-DISC-EXCEPTION (Captured via Interview)
Doc says: "No exceptions under any order type"
Code says: IF STATUS='SUSP' AND TYPE='DISC' ALLOW
Tarsius captured knowledge that was never written down!
Aha Moment #2 Phase 3

Persisted Knowledge Constrains Future AI

In a later session — after context compaction — Bob's Transformer generates code. Lifecycle hooks re-inject the approved DISC exception rule. When AI deviates, BETH catches it — vector by vector.

⚠️ Context Compaction: detailed history summarized
Lifecycle Hooks: re-inject approved rules from BRI
Result: Knowledge persists across sessions — AI never forgets!

Research Grounding Matrix

18 peer-reviewed papers and empirical surveys grounding every design decision in Tarsius

# Finding & Issue Research Source How Tarsius Addresses It
1 0–19% Behavioral Equivalence Rate AgentModernize (arXiv:2605.17535) PRE-generation verification: approve rules before code synthesis
2 Bottleneck is generation, not extraction — BSG captures 91.2% of rules, BER only 0–19% AgentModernize §V-E (arXiv:2605.17535) Operationalizes the paper's "trust boundary" as a human-in-the-loop verification layer
3 29% trust, 66% verification tax Stack Overflow 2025, METR 2025 Shifts verification from post-generation debugging to 5-min pre-gen approval
4 84% time spent on non-coding friction IDC 2025 (via Atlassian DevEx 2025) Automates rule discovery & dependency mapping to remove friction
5 LLMs fail at self-review (preservation drift) Articulate but Wrong (arXiv:2605.21537) Separates Transformer from Verifier using deterministic AST diff
6 Differential fuzzing catches 19-35% non-equiv. arXiv:2602.15761 AST structural diff provides stronger verification than random fuzzing
7 Context Rot degrades agent performance The Ralph Loop (Geoffrey Litt) Fresh-context subagent retries over persistent external state files
8 Evaluator-Optimizer pattern necessity Anthropic Engineering 2025 Subagent D AST Verifier acts as Evaluator gate for Subagent C
9 Shift to Loop Engineering Addy Osmani 2026 Designs autonomous feedback loops around LLM execution
10 IBM i skills gap #1 concern > security Fortra IBM i Survey 2026 BRI preserves business rules as git-diffable knowledge base
11 >70% of mainframe exit projects (2026) will fail due to overestimation of gen AI Gartner Press Release (June 2026) Catches the exact failure mode Gartner flags — implicit rules & contradictions before code generation
12 TSB Bank 2018 (£48.65M fine) Bank of England / FCA Report Detects the class of invisible-rule blind-spots regulators cited in a real-world bank disaster
13 Morgan Stanley COBOL AI initiative eFinancialCareers 2026 Validates extraction approach; adds missing PRE-generation verification