The Applied Agentic Framework: Architecture Decisions at Every Stage
A stage-by-stage technical guide to the architecture decisions that determine whether an agentic AI system reaches production — and stays there.
Most agentic AI architecture articles focus on what to build. This one focuses on the decisions that determine whether what you build survives contact with production. Based on 18 production agentic deployments across enterprise environments, we have identified the seven architecture decisions that most commonly determine success or failure.
Stage 1 — Semantic Foundation (Weeks 1–2)
Before writing a single line of agent code, the semantic layer must exist. An agent is only as reliable as the data it reasons over. In practice this means: all entities the agent will reference must have a single, governed definition in a dbt-modelled semantic layer; all data sources must be connected and quality-gated via Great Expectations; and a data contract must exist for every upstream feed the agent depends on. Agents built without this foundation hallucinate — not because the model is bad, but because the data is inconsistent.
Stage 2 — Orchestration Architecture (Week 2–3)
The choice of orchestration framework has long-term consequences. We use LangGraph for the majority of production deployments because its graph-based execution model makes agent state explicit and debuggable. CrewAI is appropriate for role-based multi-agent patterns where agent specialisation is the primary design concern. Avoid building custom orchestration from scratch — the complexity compounds rapidly and the maintenance burden is severe. The orchestration layer must support: deterministic replay for debugging, state persistence across sessions, and graceful degradation when tools fail.
Stage 3 — Tool Registry Design (Week 3)
Every tool an agent can call must be registered in a centralised tool registry with versioning, access controls, and usage logging. This is not optional for enterprise deployments — it is the control plane that makes governance possible. Each tool definition must include: input/output schema, latency SLA, error handling contract, and the policy conditions under which the agent is permitted to call it. A tool registry designed correctly at week three eliminates an entire class of production incidents.
Stage 4 — Policy Engine Integration (Week 4)
Agent autonomy must be bounded by a policy engine that enforces business rules at execution time. The policy engine sits between the orchestrator and tool execution — every tool call is evaluated against the policy set before it executes. Policies encode: approval workflows for high-stakes actions, rate limits per tool and per agent, data access restrictions by sensitivity classification, and audit logging requirements. Without a policy engine, an agent that encounters an edge case will make an autonomous decision your organization has not sanctioned.
Stage 5 — Evaluation Framework (Weeks 4–6)
Production readiness requires a systematic evaluation framework, not ad hoc testing. The evaluation suite must cover: golden dataset testing against known-correct outputs, adversarial prompting to test policy boundary enforcement, latency profiling under realistic load, and regression testing for every model or tool update. We use a combination of LangSmith for tracing and a custom evaluation harness that runs on every pull request. An agent that is not continuously evaluated against production-representative inputs will degrade silently.
Stage 6 — Observability Stack (Week 6–8)
Observability for agentic systems requires instrumentation at three levels: the LLM call level (token usage, latency, model version), the tool execution level (success rate, latency, error classification), and the agent execution level (task completion rate, escalation rate, human intervention frequency). These three layers feed a unified dashboard that provides real-time visibility into agent health. Alerts must be configured for anomaly detection on all three levels — an agent degradation event that goes undetected for 24 hours can cause significant downstream damage in production workflows.
Stage 7 — Production Handoff (Week 8–12)
The final stage is designed for the client's engineering team to own the system without ongoing dependency on Mission Cadre. This requires: complete architecture documentation in Architecture Decision Record format, runbook coverage for all known failure modes, a self-healing workflow design that handles the top 10 error scenarios automatically, and a structured knowledge transfer program. Technical sovereignty is not a clause in a contract — it is an engineering outcome that must be deliberately designed.
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