Knowledge Records
Every piece of captured knowledge is stored as a structured rationale record with four types:| Type | What It Captures | Example |
|---|---|---|
decision | What was chosen and why | ”Use Stripe webhooks for refund automation because manual processing doesn’t scale” |
constraint | Rules that must be respected | ”All preventive care claims must bypass cost-sharing per ACA Section 2713” |
heuristic | Learned best practices | ”Batch API calls in groups of 50 — larger batches hit rate limits” |
domain_rule | Business or regulatory rules | ”EU customers require GDPR-compliant data deletion within 30 days of request” |
- context — where this knowledge applies
- decision — what was decided or what the rule states
- rationale — why, with full reasoning
- alternatives_considered — what else was evaluated and why it was rejected
- confidence — how certain the source is (0.0–1.0)
- domain — searchable domain tag (e.g.
billing,compliance,infrastructure) - tags — additional searchable labels
How Knowledge Gets Captured
Human-in-the-Loop
The highest-signal capture mechanism. When Fliiq surfaces a question during a task, it has identified genuine uncertainty. Your response — the domain knowledge, business context, or preference you share — gets persisted as a structured record. The agent reflects back its understanding, you confirm, and it’s saved. This feels like a natural part of the agent confirming it understood you. The only difference is the confirmed understanding gets persisted permanently.Agent Decisions
When the agent makes a non-trivial decision during execution, it can emit a rationale record. Not every decision gets captured — only when the agent is weighing multiple approaches against constraints, reasoning about why something should work a certain way, or making an assumption it cannot verify.Agent Inference
The agent can also infer knowledge from patterns it observes. If it encounters the same constraint repeatedly, it can synthesize a heuristic record capturing the pattern for future reference.Querying Knowledge
Fliiq automatically retrieves relevant knowledge at the right moments:- Pre-task: When starting work, Fliiq loads domain-relevant knowledge based on the task description.
- Pre-modification: Before modifying a file, Fliiq checks for rationale records anchored to that code.
- On uncertainty: When confidence drops, Fliiq checks existing knowledge before asking you.
knowledge_query skill:
Code Anchoring & Drift Detection
Knowledge records can be anchored to specific files and line ranges. When you anchor a record, Fliiq computes a SHA-256 hash of the anchored content. On subsequent retrievals, it compares the current hash against the stored one — if the code has changed, the record is flagged as potentially stale. This is the “don’t break things you don’t understand” mechanism. Before modifying code with associated rationale, the agent reads and considers the knowledge. If the proposed change would violate a recorded constraint, it flags this rather than proceeding blindly.Skills Reference
| Skill | Purpose |
|---|---|
knowledge_save | Save a structured knowledge record with rationale, alternatives, and optional anchoring |
knowledge_query | Search by keyword, domain, file anchor, or tags — results ranked by relevance and confidence |
knowledge_update | Update confidence, text, or tags on an existing record; soft-delete expired records |