AI search is table stakes. The difference is the infrastructure that runs it — intent routers that classify fan queries, delegation layers that dispatch to specialized agents, autonomous loops that self-improve embeddings from real usage data, and MCP bridges that connect to Storyteller's SDK without fragile API integrations.
Four agent types work together. Each has a specific job. The orchestration layer routes work, delegates tasks, and monitors results — without human initiation.
Classifies every fan query by type: stat lookup, narrative search, comparison, timeline, highlight reel. Routes to the correct agent. Handles ambiguous queries by spawning parallel searches and merging results.
Spawns specialized sub-agents for each query dimension. "LeBron vs Boston playoffs" triggers: a stat agent, a narrative agent, a video highlight agent. All three run simultaneously. Results merge before the fan sees anything.
Monitors which search results fans click. Identifies gaps where no good story exists. Proposes new embedding strategies. Updates the vector index without human intervention. FAISS vector memory retains learnings across sessions.
Connects agents to Storyteller's SDK, NBA stats APIs, and content libraries. Agents call these tools directly — no middleware, no manual API integration per data source. New data sources are added as MCP servers, not as code changes.
Agent Zero monitors failed searches — queries where no story ranked above threshold. It logs the gap, checks if the content exists elsewhere in the library with different metadata, and flags what's truly missing. The editorial team gets a daily gap report they never asked for.
The system learns which embedding strategies produce the highest click-through. A "Giannis dunk" query that fans ignore gets re-weighted. A "LeBron clutch" query format that drives engagement gets propagated across all player pages. No ML team required.
What the system learns from NBA fan behavior — which query types work, which embeddings perform — transfers to NFL, MLB, and WNBA deployments automatically. The agentic infrastructure is league-agnostic. Only the content index changes per client.
Every search result, every agent decision, every embedding update logged with SHA-256 hash chaining. Content partners can verify their stories are being surfaced correctly. No black-box algorithm. Every result traceable to its source story and the agent that selected it.
Every data source inventoried with authority classification. Storyteller SDK mapped. NBA stats APIs catalogued. Conflicts documented. Missing context flagged. Agent routing logic designed against verified source material, not assumptions.
Intent router deployed. Parallel agent dispatch configured. MCP servers wired to Storyteller SDK and NBA APIs. Vector database seeded with story embeddings. Baseline metrics established for every query type.
Agent Zero monitoring deployed. Content gap detection active. Embedding self-optimization running in shadow mode — logging recommendations, not applying them yet. Watchdog recovery configured with exponential backoff.
Agentic search goes live on the sports client app. Embedding self-optimization graduates from shadow to active. Content gap reports begin delivering to editorial. A/B test measures: search engagement, story rediscovery, content gap closure rate. The system improves itself from here.