Institutional Knowledge System (IKS)

Most AI agents treat every session as a blank slate. They can follow instructions, but they have no persistent understanding of why decisions were made, what constraints drove a particular approach, or what business rules informed a specific choice. Every session starts from zero institutional understanding. IKS solves this. It captures decision rationale as a natural byproduct of doing work — when agents ask you for guidance, when they make non-trivial decisions, and when domain-specific reasoning surfaces during execution.

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”

Each record includes:

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.

You can also query explicitly using the knowledge_query skill:

Search for all billing-related constraints
→ fliiq uses knowledge_query with domain="billing", type="constraint"

Find knowledge anchored to a specific file
→ fliiq uses knowledge_query with anchor_file="src/api/payments.py"

Search by keyword
→ fliiq uses knowledge_query with keyword="refund"

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

Storage

Knowledge records are stored locally:

~/.fliiq/knowledge/
├── records/          # One JSON file per record (UUID-based)
└── index/
    ├── domain.json   # Domain → record ID lookup
    ├── tags.json     # Tag → record ID lookup
    └── anchors.json  # File path → record ID lookup

All data stays on your machine. No cloud sync, no external dependencies.

Getting Started

Guides

Skills

Daemon & Jobs

Channels

Institutional Knowledge System (IKS)

Knowledge Records

How Knowledge Gets Captured

Human-in-the-Loop

Agent Decisions

Agent Inference

Querying Knowledge

Code Anchoring & Drift Detection

Skills Reference

Storage

​Knowledge Records

​How Knowledge Gets Captured

​Human-in-the-Loop

​Agent Decisions

​Agent Inference

​Querying Knowledge

​Code Anchoring & Drift Detection

​Skills Reference

​Storage

Knowledge Records

How Knowledge Gets Captured

Human-in-the-Loop

Agent Decisions

Agent Inference

Querying Knowledge

Code Anchoring & Drift Detection

Skills Reference

Storage