Project 5: Knowledge Ledger
Build an append-only ledger that stores validated facts with provenance and retraction policies.
Quick Reference
| Attribute | Value |
|---|---|
| Difficulty | Level 3 |
| Time Estimate | 12-20 hours |
| Language | Python (Alternatives: TypeScript, Go) |
| Prerequisites | Data modeling, validation workflows |
| Key Topics | Provenance, versioning, auditability |
1. Learning Objectives
By completing this project, you will:
- Design a ledger schema that captures provenance.
- Implement append-only storage with versioning.
- Add validation gates for new entries.
- Support retractions and corrections without deleting history.
2. Theoretical Foundation
2.1 Core Concepts
- Append-Only Logs: Preserve history for auditability.
- Provenance: Track who added a fact and why.
- Retractions: Correct errors without erasing history.
2.2 Why This Matters
Multi-agent systems need trustworthy memory. A ledger ensures facts are tracked with sources and corrections are transparent.
2.3 Historical Context / Background
Event sourcing and audit logs are established patterns in distributed systems and compliance environments.
2.4 Common Misconceptions
- “Deleting bad data is enough.” Audit trails require history.
- “Provenance is optional.” It is essential for trust.
3. Project Specification
3.1 What You Will Build
A knowledge ledger that stores facts with evidence, approves them through review, and supports corrections via new entries.
3.2 Functional Requirements
- Ledger Schema: Required fields for provenance.
- Validation Gate: Reviewer approval before commit.
- Versioning: Each entry has a version or timestamp.
- Retraction Policy: New entries can invalidate old ones.
3.3 Non-Functional Requirements
- Auditability: Full history preserved.
- Consistency: Contradictions are flagged.
- Usability: Queries show most recent valid facts.
3.4 Example Usage / Output
$ ledger-add --fact "X is true" --evidence "link"
[Ledger] entry staged
[Reviewer] approved entry v5
3.5 Real World Outcome
A user can query any fact and see its source, history, and whether it was later corrected or retracted.
4. Solution Architecture
4.1 High-Level Design
Agent -> Validator -> Ledger (append-only) -> Query View
4.2 Key Components
| Component | Responsibility | Key Decisions |
|---|---|---|
| Ledger Store | Persist facts | Append-only format |
| Validator | Approve entries | Reviewer role |
| Retraction Manager | Invalidate facts | New entry flagging |
| Query Layer | Show latest valid facts | Version-based filtering |
4.3 Data Structures
Pseudo-structures:
STRUCT LedgerEntry:
entry_id
content
evidence_links
status
supersedes_entry_id
4.4 Algorithm Overview
Ledger Update
- Stage new entry.
- Validate evidence.
- Commit with version.
- If retraction, link to prior entry.
Complexity Analysis:
- Time: O(E) entries
- Space: O(E) history
5. Implementation Guide
5.1 Development Environment Setup
Use a local file-based store or lightweight database for ledger entries.
5.2 Project Structure
project-root/
├── ledger/
├── validation/
├── retractions/
├── queries/
└── logs/
5.3 The Core Question You’re Answering
“How do I make shared memory trustworthy in a multi-agent system?”
5.4 Concepts You Must Understand First
- Event sourcing
- Why append-only storage improves audits.
- Book Reference: “Designing Data-Intensive Applications” - Ch. 3
- Provenance
- How to capture source metadata.
- Book Reference: “Patterns of Enterprise Application Architecture” - Ch. 10
5.5 Questions to Guide Your Design
- Entry schema
- What fields are mandatory?
- Retractions
- How do you invalidate old facts?
5.6 Thinking Exercise
Draw a timeline showing how a fact is added, challenged, and corrected.
5.7 The Interview Questions They’ll Ask
- “Why use append-only logs in agent memory?”
- “What is provenance and why is it critical?”
- “How do you handle retractions?”
- “How do you query the latest truth?”
- “What is the risk of mutable memory?”
5.8 Hints in Layers
Hint 1: Append-only first Never delete; add new entries.
Hint 2: Add status flags Approved, rejected, retracted.
Hint 3: Add supersedes links Connect retractions to prior entries.
Hint 4: Query view Filter to most recent valid entry.
5.9 Books That Will Help
| Topic | Book | Chapter |
|---|---|---|
| Logs and consistency | “Designing Data-Intensive Applications” | Ch. 3-5 |
5.10 Implementation Phases
Phase 1: Foundation (3-4 hours)
Goals:
- Define ledger schema
- Build append-only storage
Tasks:
- Create entry structure
- Store entries with timestamps
Checkpoint: Entries are appended, not overwritten.
Phase 2: Core Functionality (4-6 hours)
Goals:
- Add validation workflow
- Add query layer
Tasks:
- Validate before commit
- Query latest valid entries
Checkpoint: Only approved entries appear in query results.
Phase 3: Polish & Edge Cases (3-4 hours)
Goals:
- Add retractions
- Add conflict flags
Tasks:
- Implement supersedes links
- Flag contradictions
Checkpoint: Retractions are visible and auditable.
5.11 Key Implementation Decisions
| Decision | Options | Recommendation | Rationale |
|---|---|---|---|
| Storage model | Mutable vs append-only | Append-only | Auditability |
| Retractions | Delete vs supersede | Supersede | Preserve history |
6. Testing Strategy
6.1 Test Categories
| Category | Purpose | Examples |
|---|---|---|
| Unit Tests | Schema validation | Missing evidence fails |
| Integration Tests | Retraction flow | Superseded entry marked |
| Edge Case Tests | Conflicts | Contradictions flagged |
6.2 Critical Test Cases
- Entry without evidence is rejected.
- Retraction supersedes prior entry.
- Query shows latest valid entry only.
6.3 Test Data
Entry 1: Fact A true
Entry 2: Retraction of Fact A
Expected: Fact A marked invalid
7. Common Pitfalls & Debugging
7.1 Frequent Mistakes
| Pitfall | Symptom | Solution |
|---|---|---|
| Deleting entries | Lost history | Use supersedes links |
| Missing provenance | Untrusted facts | Require evidence fields |
| Conflicts ignored | Inconsistent memory | Add conflict flags |
7.2 Debugging Strategies
- Trace entry lineage via supersedes links.
- Compare contradictory entries to resolve.
7.3 Performance Traps
- Ledger growth may slow queries; use indexing or snapshot views.
8. Extensions & Challenges
8.1 Beginner Extensions
- Add entry categories.
- Add simple filters.
8.2 Intermediate Extensions
- Add confidence scores.
- Add reviewer comments.
8.3 Advanced Extensions
- Build a knowledge graph export.
- Add automated contradiction repair.
9. Real-World Connections
9.1 Industry Applications
- Compliance-driven knowledge bases
- Multi-agent analytics systems
9.2 Related Open Source Projects
- Event sourcing frameworks
- Knowledge graph tools
9.3 Interview Relevance
- Provenance, audit logs, and append-only designs appear in system design interviews.
10. Resources
10.1 Essential Reading
- “Designing Data-Intensive Applications” - log-based systems
10.2 Tools & Documentation
- FIPA ACL Specification: http://www.fipa.org/specs/fipa00061/
10.3 Related Projects in This Series
- Previous Project: Negotiation & Conflict Lab (P04)
- Next Project: Tool Safety Gatekeeper (P06)
11. Self-Assessment Checklist
11.1 Understanding
- I can explain why append-only logs improve trust
11.2 Implementation
- Ledger entries are validated and auditable
11.3 Growth
- I can design retraction policies
12. Submission / Completion Criteria
Minimum Viable Completion:
- Ledger stores entries with provenance
Full Completion:
- Validation and retraction workflows added
Excellence (Going Above & Beyond):
- Knowledge graph view and conflict repair added
This guide was generated from LEARN_COMPLEX_MULTI_AGENT_SYSTEMS_DEEP_DIVE.md. For the complete learning path, see the README.