Q&A: Graph Federation in Sapient.x — How Does the Knowledge Graph Scale Across AGI Corp's Distributed Repos?

[AGI-Corporation]

Overview

This Q&A explores how Sapient.x manages graph federation — maintaining a unified, consistent knowledge graph while ingesting data from 8+ independent AGI Corp repositories in real time.

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Q1: What is graph federation in the context of Sapient.x?

A: Graph federation in Sapient.x means the knowledge graph can query and reason across multiple sub-graphs without requiring full data centralization. Each AGI Corp repo acts as a federated data source with its own schema fragment, and Sapient.x's federation layer stitches them into a unified queryable ontology.

Federation modes:

Mode	Description	Use Case
Pull ingestion	Sapient.x polls repo APIs on a schedule	Batch updates (epoch events, compliance records)
Push webhook	Repos push events to Sapient.x in real time	High-frequency updates (route events, guardrails alerts)
Virtual federation	Queries routed live to source repo APIs	Low-latency lookups without full graph copy
Materialized views	Cached subgraph snapshots for heavy analytics	Compliance reporting, spatial analytics

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Q2: How does Sapient.x handle schema conflicts across repos?

A: Each repo registers a schema fragment with Sapient.x at startup:

# evolution-agent schema registration
sapient_client.register_schema(
    source="evolution-agent",
    nodes=["EpochEvent", "Capability", "MutationRecord"],
    edges=["EVOLVED_FROM", "HAS_CAPABILITY"],
    conflict_resolution="source_wins"  # evolution-agent owns its own types
)

Conflict resolution strategies:

• source_wins: The originating repo's schema definition takes precedence
• merge: Sapient.x merges compatible schema fragments
• version_namespace: Conflicting types get namespaced (e.g., evolution_agent.Agent vs routex.Agent)
• human_review: Schema conflicts above severity threshold trigger admin review

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Q3: What is the data consistency model across the federated graph?

A: Sapient.x uses eventual consistency with conflict-free replicated data types (CRDTs) for most graph operations:

consistency_model:
  default: eventual
  strong_consistency_nodes:
    - ComplianceRecord  # CMMC audit integrity
    - SafetyPolicy      # guardrails- policy accuracy
  crdt_types:
    - counter: agent_query_count
    - set: capability_registry
    - lww_register: agent_status  # Last-write-wins
  sync_interval_ms: 5000
  conflict_resolution: vector_clock

For CMMC compliance records and guardrails- safety policies, strong consistency is enforced with distributed locking.

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Q4: How are cross-repo graph traversals executed efficiently?

A: Sapient.x uses a query planner that optimizes cross-repo traversals:

// Example: Find all routes that use agents with CMMC compliance issues
MATCH (route:Route)-[:SERVED_BY]->(agent:Agent)
MATCH (agent)-[:HAS_COMPLIANCE_RECORD]->(record:ComplianceRecord)
WHERE record.status = 'NON_COMPLIANT'
RETURN route.id, agent.id, record.domain, record.control_id

Query planner steps:

1. Locality analysis: Identifies which nodes/edges live in which federated source
2. Push-down filters: Applies WHERE clauses at source before joining
3. Parallel sub-queries: Executes independent federation queries in parallel
4. Join optimization: Uses cached materialized views for common join patterns
5. Result caching: Caches query results with configurable TTL (default: 30s)

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Q5: How does real-time event streaming work into the graph?

A: Repos publish events via NANDA, and Sapient.x has a dedicated stream ingestion pipeline:

from sapient_x.ingestion import StreamIngester

ingester = StreamIngester()

@ingester.on_event("epoch_committed", source="evolution-agent")
def handle_epoch(event: dict):
    # Create EpochEvent node
    graph.merge(
        node_type="EpochEvent",
        match_on=["version"],
        properties=event["payload"]
    )
    # Link to Agent node
    graph.create_edge(
        from_node=("Agent", {"id": event["agent_id"]}),
        to_node=("EpochEvent", {"version": event["payload"]["version"]}),
        edge_type="EVOLVED_FROM"
    )

Ingestion latency targets: < 500ms for webhook events, < 5s for NANDA broadcast events.

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Q6: How does Sapient.x ensure graph integrity during a partial federation failure?

A: Sapient.x implements a circuit breaker pattern per federated source:

circuit_breaker_per_source:
  evolution-agent:
    failure_threshold: 5  # consecutive failures
    reset_timeout_s: 60
    fallback: use_cached_snapshot
  guardrails:
    failure_threshold: 3
    reset_timeout_s: 30
    fallback: block_safety_queries  # fail safe for safety policies
  route_x:
    failure_threshold: 10
    reset_timeout_s: 120
    fallback: use_cached_snapshot

When a source is circuit-broken, Sapient.x:

1. Serves cached data with a data_freshness header indicating staleness
2. Queues incoming events for replay when source recovers
3. Broadcasts federation_degraded event via NANDA to notify downstream agents

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Related Wiki: Sapient.x: Spatial Intelligence Guide
Related Discussions: Spatial Graph Architecture, x402 Payments Integration