How CAKE and Amazon Bedrock AgentCore turn siloed customer data into seconds‑fast insights

Sales teams waste hours hopping between dashboards, notes, and tickets to answer one customer question. CAKE — Customer Agent & Knowledge Engine — built on Amazon Bedrock AgentCore, consolidates those signals into a single conversational surface that returns explainable answers in seconds. It’s an example of how AI agents and multi-agent systems can turn fragmented data into actionable customer intelligence for sales, product, and support teams.

TL;DR

CAKE is a production-grade multi-agent customer intelligence system that orchestrates parallel retrievers (a knowledge graph, low-latency metrics, semantic document search, and external web queries) through Amazon Bedrock AgentCore. An offline pipeline precomputes metrics and builds a knowledge graph so runtime agents focus on fast retrieval. The result: most queries return in under 10 seconds (DynamoDB metric lookups often <10ms), with deterministic row-level security and auditable inference via GraphRAG chains. Practical tradeoffs: embedding and inference costs, governance, and some operational complexity.

“Salespeople spent hours hopping between dashboards; CAKE consolidates those views so they can get answers in seconds.”

What CAKE is (plain language)

Think of Amazon Bedrock AgentCore as an air-traffic controller for AI agents and tools. CAKE uses that controller to coordinate specialists: a graph database that understands relationships, a lightning-fast key-value store for precomputed KPIs, a semantic search for notes and docs, and web lookup tools for external signals. A supervisor agent reads the user intent, fires off parallel retrievals, streams partial results, and then synthesizes a single, explainable answer.

Key building blocks

• Amazon Bedrock AgentCore — runtime/orchestration for multi-agent systems: supervisor, parallel execution, conversation state, and tool routing.
• Amazon Neptune — knowledge graph for semantic relationships and multi-hop reasoning.
• Amazon DynamoDB — precomputed customer metrics with sub-10ms lookups for low-latency answers.
• Amazon OpenSearch Service — semantic search over documents and field notes (embeddings-based).
• Amazon Redshift & S3 — analytical source and storage for the offline ETL/embedding pipeline.
• Row-Level Security (RLS) tool — deterministic permission checks applied at query time.

How the multi-agent architecture works (technical lane)

First, a user asks a question in natural language. The supervisor agent performs intent analysis and parallelizes the work across retriever tools:

• Metric lookup in DynamoDB for recent KPIs.
• Multi-hop traversal in Neptune to fetch related accounts, product links, or organizational structure.
• Semantic search in OpenSearch to pull recent support tickets, meeting notes, and field comments.
• Optional external web search for competitive or public intel.

Partial results stream back to the supervisor, which uses GraphRAG (graph-based retrieval-augmented generation) to create a deterministic, auditable path from source evidence to the final answer. The supervisor formats the output (via helpers such as a table-to-text agent) and returns a unified, explainable response.

“Amazon Bedrock AgentCore provides the runtime features multi-agent systems need — inter-agent communication, parallel execution, conversation state tracking, and tool routing — as a managed service.”

What this means for your team: parallel tool orchestration reduces end-to-end latency by avoiding serial data fetching and by keeping heavy aggregation in offline pipelines.

Offline pipeline and data cadence

Heavy transformations run on a scheduled ETL pipeline: Redshift exports → transform → load into Neptune, DynamoDB, and OpenSearch. Large documents go through S3/Parquet and are embedded before indexing. This separation lets runtime agents deliver low-latency answers without recomputing expensive joins on-demand.

Concrete example: a salesperson’s query traced

Sample query: “What’s the churn risk for Acme Corp and why?”

1. Supervisor classifies intent: risk analysis + explanation.
2. DynamoDB returns precomputed churn_score for Acme in ~5–10ms.
3. Neptune traverses the account graph to find related accounts, contract dates, and product entitlements (multi-hop traversal).
4. OpenSearch finds three recent support tickets and two account exec notes mentioning “billing dispute” and “performance issues.”
5. Web search pulls a public news item about a competitor price cut.
6. RLS enforces access: the rep only receives fields they’re allowed to see (no PII leakage).
7. GraphRAG records the retrieval chain; the supervisor synthesizes a short, sourced summary and recommended next steps.

Result served in ~7 seconds (load-test median), with provenance pointers: churn_score source (DynamoDB), ticket IDs, Neptune traversal path. The rep sees both the number and the “why.”

Business impact & measurable outcomes

CAKE’s primary value is time-to-insight. Replacing manual aggregation across dashboards and notes yields:

• Reduced time-to-answer for routine queries from hours to seconds.
• Faster, evidence-backed sales conversations and shorter deal cycles.
• Higher rep productivity — fewer context switches and better preparation.
• Improved feedback loops between sales and product via structured summaries.

Key metrics to measure in a pilot: time saved per query, dashboard hops avoided, conversion uplift on prioritized plays, and total cost per query (inference + retrieval). A practical pilot focuses on a single sales play or account segment to make ROI visible in 60–90 days.

Governance, explainability, and risk controls

Deterministic access control and traceability are central to CAKE’s design.

• Row-level security (RLS) is enforced at the data layer so permissioning doesn’t depend on heuristic model behavior.
• GraphRAG & provenance produce auditable inference paths: every claim points to the original retriever hit(s) and document IDs.
• Model-hopping & fallbacks preserve availability: primary model → secondary model → cached or templated response if necessary.
• Human-in-the-loop validation is required for high-risk outputs (contracts, legal language, or PII exposure).

“Neptune captures the semantic relationships that let agents explain not just what metrics are, but why they matter in a business context.”

Practical observability: log retriever calls, model prompts and responses, latency by component, token counts, and RLS decision traces. Critical alerting should include elevated hallucination indicators (disagreement across retrievers), RLS policy errors, and tail-latency spikes.

Sample audit log fields

• timestamp
• user_id and role
• query_text
• retriever_hits (IDs + confidence)
• Neptune traversal path
• model_version and token_count
• final_response + provenance links
• RLS decision metadata

Cost drivers and control patterns

Major ongoing costs come from embeddings storage, semantic search operations, and LLM inference. Control strategies include:

• Precompute high-value KPIs in DynamoDB to avoid repeated inference for numeric lookups.
• Tier embeddings: warm frequently-used vectors in optimized indices and archive cold data.
• Token optimization and prompt engineering to reduce per-response token consumption.
• Cache templated responses for highly repetitive queries and use confidence thresholds before invoking expensive models.

Estimate total cost by combining: monthly embedding storage, vector search ops (per query), DynamoDB read costs (proportional to QPS), and per-token inference costs. Pilots should instrument cost per query early and set guardrails.

Portability and vendor lock-in: a pragmatic stance

Relying on managed services accelerates delivery, but lock-in concerns are real. Reduce risk by:

• Abstracting retrievers with a standard interface so backends (Neptune, other graphs) can be swapped.
• Keeping embeddings and metadata in an exchangeable format (Parquet, vector formats) and documenting indexing cadence.
• Using open-source RAG patterns (GraphRAG concepts are portable) and avoiding proprietary prompt-only logic embedded deeply in the data layer.

Adoption checklist: a 90‑day pilot playbook

• Week 0–2 — Identify target use case (e.g., churn-risk queries for top 100 accounts). Map data sources and permission boundaries.
• Week 2–6 — Build ETL: export from Redshift → transform → load Neptune, DynamoDB, OpenSearch. Index embeddings for recent documents.
• Week 6–10 — Stand up Bedrock AgentCore flows: supervisor, retrievers, GraphRAG chains, and RLS enforcement. Implement basic observability and cost tracking.
• Week 10–12 — Pilot with a small set of sales reps. Collect time-to-insight, correctness feedback, and cost-per-query. Iterate prompts and retrieval thresholds.

Questions for procurement and technical teams

• How will you enforce row-level security so users only see permitted fields?
  

  Implement RLS at the data layer and log every RLS decision. Avoid relying solely on post-generation filters in the model output.

• What happens if the primary model is throttled or fails?
  

  Use model-hopping with at least one fallback model and cache critical responses. Design confidence thresholds to route to human review when uncertain.

• How are hallucinations and inaccuracies detected?
  

  Require provenance pointers for every claim, monitor retriever agreement, and build human review workflows for high-risk outputs.

• Can we port this to a multi-cloud or hybrid setup?
  

  Yes, if retriever and embedding interfaces are abstracted and data exports use portable formats (Parquet, standard vector schemas).

When this pattern doesn’t fit

Teams with immature data estates, very small user bases, or strict regulatory constraints (where any automated synthesis is forbidden) may find the operational overhead outweighs benefits. For those situations, start with a simpler RAG setup focused on secure search and human-in-the-loop synthesis before moving to a fully agentic runtime.

Next step

Run a focused 90-day pilot on a single sales play or account segment. Instrument time-to-answer, accuracy (human-labeled), and cost-per-query. Use those metrics to decide whether to expand, tighten governance, or optimize costs.

Built by an AWS cross-functional team including product, data engineering, and applied science leaders such as Monica Jain, M. Umar Javed, Damien Forthomme, Mihir Gadgil, Sujit Narapareddy, and Norman Braddock.