Architecture in Action

The seven architectural layers described in this section are not independent components — they function as an integrated system. Understanding how they interact in a real workflow request is the best way to see how the architecture delivers reliable, governed, enterprise-grade agent behavior.

An End-to-End Request Flow

Consider a request submitted to an enterprise financial analysis agent: “Generate a quarterly variance report for the APAC region and flag any items that require CFO review.”

Here is what happens across each architectural layer:

Interface Layer (Layer 7) — The user submits the request through an embedded widget in the financial reporting application. The interface captures the request with session context (user identity, current application context, prior session summary) and forwards it to the agent system.

Foundation Model Layer (Layer 1) — The LLM receives the request along with the system prompt (defining the agent’s scope and authorized tools), the session context, and relevant information retrieved from memory. It interprets the goal, identifies the required sub-tasks, and generates a structured execution plan: retrieve Q3 and Q4 actuals from the financial data warehouse; retrieve Q4 budget from the planning system; compare actuals to budget; apply CFO escalation criteria from the policy knowledge base; generate structured report; flag items meeting escalation threshold.

Orchestration Layer (Layer 3) — The orchestration framework (in this case, LangGraph) receives the plan, validates that all proposed tool invocations are within the agent’s permitted scope, and begins executing sub-tasks in sequence. It maintains state: which sub-tasks are complete, what intermediate results have been obtained, and which sub-tasks remain.

Tool Integration Layer (Layer 2) — The agent invokes tools via MCP-compliant connectors: queries the financial data warehouse API for Q3/Q4 actuals, queries the planning system API for Q4 budget, retrieves the CFO escalation policy document from the knowledge base API. Each invocation is logged with parameters and results.

Memory Layer (Layer 4) — The memory system provides: episodic context (the CFO’s escalation preferences noted in a prior session), semantic knowledge (the current CFO escalation policy retrieved from the knowledge base), and state tracking (intermediate results as each sub-task completes). Retrieved knowledge is injected into the model’s context for the comparison and report generation steps.

Safety Layer (Layer 5) — At each step, the safety layer validates that tool invocations are within permitted scope, that retrieved data is not being exposed inappropriately, and that the agent’s proposed actions align with policy. Content filters verify that the generated report does not expose sensitive data in unauthorized ways. The CFO-escalation decision criteria are applied deterministically (not by LLM reasoning) to identify flagged items.

Observability Layer (Layer 6) — Every step produces structured log entries: tool invocations with parameters and results, memory retrievals with source metadata, the reasoning at each decision point, and the final output. A distributed trace ID links the entire workflow. Performance metrics (total latency, tool response times, number of LLM calls) are captured.

Interface Layer (Layer 7) — The agent delivers the completed report through the interface, with a summary: “Q4 APAC variance report generated. 3 line items flagged for CFO review: [items listed with variance amounts and reasoning]. Report is available for download. I have not sent any notifications — shall I draft the CFO review request?” The user reviews the flagged items, confirms the report is accurate, and approves the CFO notification draft. The HITL approval is logged with the user’s identity and timestamp.

Architecture Implementation Choices

In practice, this architecture is assembled from a combination of managed platform services and purpose-built components. No enterprise needs to build all seven layers from scratch:

Cloud platform managed services handle runtime, session management, tool invocation infrastructure, logging, and much of the security layer — AWS Bedrock AgentCore, Azure AI Foundry, or Google Vertex AI Agent Builder.

Open-source orchestration frameworks (LangGraph, LlamaIndex) provide specialized orchestration and RAG capabilities that cloud platforms may not offer natively.

Enterprise integration layer — MCP-compliant tool wrappers for enterprise systems are typically built by the organization’s integration team and registered with the platform’s tool registry.

Custom safety and content filters — Domain-specific content filtering (for instance, clinical information classification in healthcare, or regulatory content filtering in financial services) is typically custom-built on top of platform-provided filter infrastructure.

The guiding principle: leverage managed services for generic infrastructure; build custom components only where your requirements diverge from what platforms provide. This concentrates engineering effort on the elements that are genuinely differentiated for your organization.

The Architectural Maturity Path

Few organizations deploy all seven layers fully on day one. A practical maturity path:

  1. Foundation — Foundation model + basic tool integration + minimal safety (permissions, HITL for all consequential actions) + interface. Sufficient for initial pilots.
  2. Core production — Add orchestration (multi-step workflows), semantic memory (RAG knowledge base), content filtering, and structured logging. Required before scaling beyond pilot.
  3. Full governance — Add episodic and procedural memory, GraphRAG, distributed tracing, behavioral anomaly detection, and comprehensive compliance logging. Required for regulated industry deployment.
  4. Optimization — Add model routing, caching, speculative decoding, and memory-augmented cost reduction. Required when inference costs become material at scale.

Make It Your Own

Key questions to ask in the context of your organization:

  • Which cloud platform will serve as the backbone for your architecture — AWS, Azure, or GCP — and what does your implementation plan look like for each architectural layer?
  • What is the right balance between managed services and custom development for your team’s capabilities and your operational requirements?
  • Which architectural maturity level is your target for the initial pilot versus full production deployment?
  • How will you validate end-to-end request flows — including failure paths and security boundary conditions — before deploying to production?
  • What architectural review process will you establish to evaluate agent deployments before they go live — and who participates?
  • How will you evolve the architecture as agent capabilities advance and your organization’s deployment experience matures?