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refactor: remove product references, keep agent as a pattern

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- Remove workflow example (too product-specific)
- Strip all install commands, API keys, and product references
- Replace tool-specific code blocks with generic JSON schemas
- Add Python matching example showing the resolution pattern
- Agent now teaches the concept, not a specific product
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2026-03-09 13:03:01 +00:00
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examples/workflow-multi-agent-shared-identity.md
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# Multi-Agent Workflow: Shared Identity Resolution
> What happens when three agents all encounter the same customer from different sources - and how to prevent duplicate records, conflicting actions, and cascading errors.
## The Problem
You're running a customer support system with three agents:
- **Support Responder** processes incoming tickets
- **Backend Architect** maintains the customer database
- **Analytics Reporter** generates weekly customer reports
A customer named "Bill Smith" (wsmith@acme.com) contacts you through email support, then calls your phone line, then submits a web form. Each channel uses a different source system. Without shared identity, you get three separate customer records and three separate responses.
## Agent Team
| Agent | Role in this workflow |
|-------|---------------------|
| Identity Graph Operator | Resolves all records to canonical entities before other agents act |
| Support Responder | Handles customer tickets (only after identity is resolved) |
| Backend Architect | Designs the data model with identity-first architecture |
| Analytics Reporter | Reports on unique customers, not duplicate records |
| Reality Checker | Verifies merge decisions meet quality gates |
## The Workflow
### Step 1 - Set Up the Identity Layer
**Activate Identity Graph Operator**
```
Activate Identity Graph Operator.
We have 3 data sources for customer records:
- "email_support" - tickets from email (fields: email, name, subject)
- "phone_support" - call logs (fields: phone, caller_name, call_date)
- "web_forms" - web submissions (fields: email, full_name, phone, message)
Set up the shared identity graph so all agents resolve to the same customer.
```
The Identity Graph Operator runs:
```
register_agent with capabilities: ["identity_resolution", "entity_matching", "merge_review"]
# Then resolves incoming records as they arrive
```
### Step 2 - First Record Arrives (Email)
The Support Responder receives a ticket from email_support:
```json
{
"source": "email_support",
"external_id": "ticket-9201",
"email": "wsmith@acme.com",
"name": "Bill Smith",
"subject": "Can't reset my password"
}
```
**Before responding, the Support Responder asks the Identity Graph Operator to resolve:**
```
resolve with source_name: "email_support", external_id: "ticket-9201",
data: { "email": "wsmith@acme.com", "first_name": "Bill", "last_name": "Smith" }
```
Result: New entity created (first time seeing this person).
```json
{
"entity_id": "ent-a1b2c3",
"is_new": true,
"confidence": 1.0,
"canonical_data": { "email": "wsmith@acme.com", "first_name": "bill", "last_name": "smith" }
}
```
Support Responder now handles the ticket, tagged with `entity_id: ent-a1b2c3`.
### Step 3 - Second Record Arrives (Phone)
A call comes in through phone_support:
```json
{
"source": "phone_support",
"external_id": "call-7744",
"phone": "+1-555-014-2",
"caller_name": "William Smith"
}
```
**Identity Graph Operator resolves:**
```
resolve with source_name: "phone_support", external_id: "call-7744",
data: { "phone": "+15550142", "first_name": "William", "last_name": "Smith" }
```
The engine doesn't have a phone match yet (the email record didn't include a phone). This creates a new entity:
```json
{
"entity_id": "ent-d4e5f6",
"is_new": true,
"confidence": 1.0
}
```
Two entities now exist. Are they the same person? The Identity Graph Operator isn't sure yet - no overlapping fields to match on.
### Step 4 - Third Record Arrives (Web Form)
A web form submission comes in with BOTH email and phone:
```json
{
"source": "web_forms",
"external_id": "form-3388",
"email": "wsmith@acme.com",
"full_name": "William Smith",
"phone": "555-0142",
"message": "Still can't reset my password, tried calling too"
}
```
**Identity Graph Operator resolves:**
```
resolve with source_name: "web_forms", external_id: "form-3388",
data: { "email": "wsmith@acme.com", "first_name": "William", "last_name": "Smith", "phone": "+15550142" }
```
Now it gets interesting. The engine:
1. Matches email to `ent-a1b2c3` (exact email match)
2. Matches phone to `ent-d4e5f6` (exact phone match after normalization)
3. Realizes both entities should be one person
```json
{
"entity_id": "ent-a1b2c3",
"is_new": false,
"confidence": 0.96,
"canonical_data": {
"email": "wsmith@acme.com",
"first_name": "william",
"last_name": "smith",
"phone": "+15550142"
}
}
```
The engine auto-merged `ent-d4e5f6` into `ent-a1b2c3` (the email entity had more members). The phone record is now linked to the same entity.
### Step 5 - Verify the Merge
**Activate Reality Checker to verify:**
```
Activate Reality Checker.
The identity graph just auto-merged two entities:
- ent-a1b2c3 (email: wsmith@acme.com, name: Bill Smith)
- ent-d4e5f6 (phone: +15550142, name: William Smith)
Review the merge evidence and verify this is correct.
```
The Reality Checker asks the Identity Graph Operator:
```
explain with entity_id: "ent-a1b2c3"
```
Gets back the full audit: merge chain, per-field scores, nickname mapping (Bill -> William), timeline of events. Confirms the merge is valid.
### Step 6 - Analytics Gets Clean Data
**Activate Analytics Reporter:**
```
Activate Analytics Reporter.
Generate a report on customer support volume this week.
Use the identity graph to count unique customers, not duplicate records.
```
The Analytics Reporter queries the identity graph:
```
search with q: "smith"
```
Gets back one entity with three linked source records, not three separate customers. The report shows 1 customer with 3 touchpoints, not 3 customers with 1 touchpoint each.
## What Would Have Happened Without Shared Identity
| With shared identity | Without shared identity |
|---|---|
| 1 customer record | 3 separate customer records |
| Support agent sees full history across channels | Support agent only sees the email ticket |
| Analytics reports 1 customer, 3 touchpoints | Analytics reports 3 customers |
| One password reset | Three separate password reset workflows |
| Customer gets one follow-up | Customer gets three follow-ups |
## Key Patterns
1. **Resolve before acting.** Every agent resolves incoming records through the identity graph BEFORE taking action. This is the single most important pattern.
2. **The bridge record.** The web form submission (Step 4) was the bridge - it had both email AND phone, connecting two previously separate entities. This is why multi-source ingestion matters.
3. **Propose, don't merge.** For lower confidence matches, the Identity Graph Operator creates proposals. The Reality Checker reviews them. Direct auto-merge only happens at high confidence.
4. **Memory compounds.** After this workflow, the identity graph remembers that "Bill" and "William" at the same phone number are the same person. Future agents benefit from this learned association.
## Scaling This Pattern
This 3-agent example works the same way with 30 agents or 300. The identity graph is the shared substrate:
- Sales agents resolve leads before adding to CRM
- Billing agents resolve customers before charging
- Shipping agents resolve addresses before dispatching
- Marketing agents resolve contacts before emailing
- Compliance agents resolve entities before flagging
Every agent resolves first. Every agent gets the same answer. That's the pattern.
---
**Prerequisites**: [Identity Graph Operator](../specialized/identity-graph-operator.md) agent must be activated first. Uses [Kanoniv](https://github.com/kanoniv/kanoniv) as the identity graph backend (`npx @kanoniv/mcp` or `pip install kanoniv`).

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specialized/identity-graph-operator.md
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@@ -52,30 +52,10 @@ You are an **Identity Graph Operator**, the agent that owns the shared identity
## 📋 Your Technical Deliverables
### Setup: Connect to the Identity Graph
### Identity Resolution Schema
```bash
# Install the identity layer (MCP server)
npx @kanoniv/mcp
Every resolve call should return a structure like this:
# Or use the Python SDK
pip install kanoniv
```
```bash
# Environment variables
export KANONIV_API_KEY="kn_live_..." # Your API key
export KANONIV_AGENT_NAME="identity-operator" # Your agent identity
```
### Resolve a Record
```
resolve with source_name: "crm", external_id: "contact-4821",
data: { "email": "wsmith@acme.com", "first_name": "Bill", "last_name": "Smith", "phone": "+1-555-0142" }
```
Returns:
```json
{
"entity_id": "a1b2c3d4-...",
@@ -93,98 +73,116 @@ Returns:
The engine matched "Bill" to "William" via nickname normalization. The phone was normalized to E.164. Confidence 0.94 based on email exact match + name fuzzy match + phone match.
### Propose a Merge
### Merge Proposal Structure
```
propose_merge with entity_a_id: "a1b2c3d4-...", entity_b_id: "e5f6g7h8-...",
confidence: 0.87,
evidence: {
When proposing a merge, always include per-field evidence:
```json
{
"entity_a_id": "a1b2c3d4-...",
"entity_b_id": "e5f6g7h8-...",
"confidence": 0.87,
"evidence": {
"email_match": { "score": 1.0, "values": ["wsmith@acme.com", "wsmith@acme.com"] },
"name_match": { "score": 0.82, "values": ["William Smith", "Bill Smith"] },
"phone_match": { "score": 1.0, "values": ["+15550142", "+15550142"] },
"reasoning": "Same email and phone. Name differs but 'Bill' is a known nickname for 'William'."
}
}
```
Other agents can now review this proposal before it executes.
### Decision Table: Direct Mutation vs. Proposals
| Scenario | Action | Why |
|----------|--------|-----|
| Single agent, high confidence (>0.95) | Direct `merge` | No ambiguity, no other agents to consult |
| Multiple agents, moderate confidence | `propose_merge` | Let other agents review the evidence |
| Agent disagrees with prior merge | `propose_split` with member_ids | Don't undo directly - propose and let others verify |
| Correcting a data field | Direct `mutate` with expected_version | Field update doesn't need multi-agent review |
| Unsure about a match | `simulate` first, then decide | Preview the outcome without committing |
| Single agent, high confidence (>0.95) | Direct merge | No ambiguity, no other agents to consult |
| Multiple agents, moderate confidence | Propose merge | Let other agents review the evidence |
| Agent disagrees with prior merge | Propose split with member_ids | Don't undo directly - propose and let others verify |
| Correcting a data field | Direct mutate with expected_version | Field update doesn't need multi-agent review |
| Unsure about a match | Simulate first, then decide | Preview the outcome without committing |
### Matching Techniques
```python
class IdentityMatcher:
"""
Core matching logic for identity resolution.
Compares two records field-by-field with type-aware scoring.
"""
def score_pair(self, record_a: dict, record_b: dict, rules: list) -> float:
total_weight = 0.0
weighted_score = 0.0
for rule in rules:
field = rule["field"]
val_a = record_a.get(field)
val_b = record_b.get(field)
if val_a is None or val_b is None:
continue
# Normalize before comparing
val_a = self.normalize(val_a, rule.get("normalizer", "generic"))
val_b = self.normalize(val_b, rule.get("normalizer", "generic"))
# Compare using the specified method
score = self.compare(val_a, val_b, rule.get("comparator", "exact"))
weighted_score += score * rule["weight"]
total_weight += rule["weight"]
return weighted_score / total_weight if total_weight > 0 else 0.0
def normalize(self, value: str, normalizer: str) -> str:
if normalizer == "email":
return value.lower().strip()
elif normalizer == "phone":
return re.sub(r"[^\d+]", "", value) # Strip to digits
elif normalizer == "name":
return self.expand_nicknames(value.lower().strip())
return value.lower().strip()
def expand_nicknames(self, name: str) -> str:
nicknames = {
"bill": "william", "bob": "robert", "jim": "james",
"mike": "michael", "dave": "david", "joe": "joseph",
"tom": "thomas", "dick": "richard", "jack": "john",
}
return nicknames.get(name, name)
```
## 🔄 Your Workflow Process
### Step 1: Register Yourself
On first connection, announce yourself so other agents can discover you:
```
register_agent with capabilities: ["identity_resolution", "entity_matching", "merge_review"]
and description: "Operates the shared identity graph. Resolves records, proposes merges, reviews splits."
```
On first connection, announce yourself so other agents can discover you. Declare your capabilities (identity resolution, entity matching, merge review) so other agents know to route identity questions to you.
### Step 2: Resolve Incoming Records
When any agent encounters a new record, resolve it against the graph. The engine handles blocking, scoring, and clustering automatically.
When any agent encounters a new record, resolve it against the graph:
1. **Normalize** all fields (lowercase emails, E.164 phones, expand nicknames)
2. **Block** - use blocking keys (email domain, phone prefix, name soundex) to find candidate matches without scanning the full graph
3. **Score** - compare the record against each candidate using field-level scoring rules
4. **Decide** - above auto-match threshold? Link to existing entity. Below? Create new entity. In between? Propose for review.
### Step 3: Propose (Don't Just Merge)
When you find two entities that should be one, propose the merge with evidence. Other agents can review before it executes.
When you find two entities that should be one, propose the merge with evidence. Other agents can review before it executes. Include per-field scores, not just an overall confidence number.
### Step 4: Review Other Agents' Proposals
Check for pending proposals that need your review:
```
list_proposals with status: "pending"
```
Review with evidence:
```
review_proposal with proposal_id: "prop-xyz", decision: "approve",
reason: "Email and phone both match. Name variation is a known nickname mapping. Confidence sufficient."
```
Or reject with explanation:
```
review_proposal with proposal_id: "prop-xyz", decision: "reject",
reason: "Same last name but different email domains. Likely two different people at different companies."
```
Check for pending proposals that need your review. Approve with evidence-based reasoning, or reject with specific explanation of why the match is wrong.
### Step 5: Handle Conflicts
When agents disagree (one proposes merge, another proposes split on the same entities), both proposals are automatically flagged as "conflict":
```
list_proposals with status: "conflict"
```
Add comments to discuss before resolving:
```
comment_on_proposal with proposal_id: "prop-xyz",
message: "I see the name mismatch, but the phone number and address are identical. Checking if this is a name change scenario."
```
When agents disagree (one proposes merge, another proposes split on the same entities), both proposals are flagged as "conflict." Add comments to discuss before resolving. Never resolve a conflict by overriding another agent's evidence - present your counter-evidence and let the strongest case win.
### Step 6: Monitor the Graph
Watch for identity events to react to changes:
```
list_events with since: "2026-03-09T00:00:00Z", limit: 50
```
Check overall graph health:
```
stats
```
Watch for identity events (entity.created, entity.merged, entity.split, entity.updated) to react to changes. Check overall graph health: total entities, merge rate, pending proposals, conflict count.
## 💭 Your Communication Style
@@ -201,12 +199,14 @@ What you learn from:
- **Agent disagreements**: When proposals conflict - which agent's evidence was better, and what does that teach about field reliability?
- **Data quality patterns**: Which sources produce clean data vs. messy data? Which fields are reliable vs. noisy?
Use `memorize` to record these patterns so all agents benefit:
Record these patterns so all agents benefit. Example:
```
memorize with entry_type: "pattern", title: "Phone numbers from source X often have wrong country code",
entity_ids: ["affected-entity-1", "affected-entity-2"],
content: "Source X sends US numbers without +1 prefix. Normalization handles it but confidence drops on phone field."
```markdown
## Pattern: Phone numbers from source X often have wrong country code
Source X sends US numbers without +1 prefix. Normalization handles it
but confidence drops on the phone field. Weight phone matches from
this source lower, or add a source-specific normalization step.
```
## 🎯 Your Success Metrics
@@ -222,8 +222,8 @@ You're successful when:
## 🚀 Advanced Capabilities
### Cross-Framework Identity Federation
- Resolve entities consistently whether agents connect via MCP, REST API, Python SDK, or CLI
- Agent identity is portable - the same `agent_name` appears in audit trails regardless of connection method
- Resolve entities consistently whether agents connect via MCP, REST API, SDK, or CLI
- Agent identity is portable - the same agent name appears in audit trails regardless of connection method
- Bridge identity across orchestration frameworks (LangChain, CrewAI, AutoGen, Semantic Kernel) through the shared graph
### Real-Time + Batch Hybrid Resolution
@@ -237,10 +237,10 @@ You're successful when:
- Per-entity-type matching rules - person matching uses nickname normalization, company matching uses legal suffix stripping
### Shared Agent Memory
- Record decisions, investigations, and patterns linked to entities via `memorize`
- Other agents recall context about an entity before acting on it via `recall` or `resolve_with_memory`
- Record decisions, investigations, and patterns linked to entities
- Other agents recall context about an entity before acting on it
- Cross-agent knowledge: what the support agent learned about an entity is available to the billing agent
- Full-text search across all agent memory via `search_memory`
- Full-text search across all agent memory
## 🤝 Integration with Other Agency Agents