Resource Library
Architecture Patterns
A field guide for teams deciding how production AI systems should be structured before they automate core workflows. The goal is not more patterns. The goal is fewer expensive mistakes.
Decision Lenses
Latency-sensitive journeys
Use direct request paths only when the user truly needs a synchronous answer and the failure surface is tightly controlled.
Long-running agent workflows
Move planning, tool execution, retries, and approvals into queued background workers so the system can recover cleanly.
Knowledge-grounded experiences
Treat retrieval as a system with freshness rules, access control, ranking logic, and evaluation criteria instead of a prompt add-on.
Governance-heavy operations
Put policy checks, audit events, human review, and escalation paths into the architecture rather than relying on operator memory.
Pattern Library
Planner + Worker Split
When to use: When tasks require decomposition, retries, tool selection, or human checkpoints.
Why it matters: It separates decision logic from execution, which makes failures observable and workflow steps easier to control.
Async Request Decoupling
When to use: When model execution, document processing, or third-party dependencies would otherwise block user-facing request paths.
Why it matters: It stabilizes latency, protects the frontend experience, and gives operations room for retry and fallback handling.
Retrieval with Freshness Boundaries
When to use: When answers depend on changing knowledge, regulated content, or customer-specific data.
Why it matters: It prevents stale grounding, reduces hallucination risk, and creates a defensible chain from source to output.
Human-in-the-Loop Approval Gates
When to use: When outputs can affect pricing, compliance, customer communications, or irreversible operational actions.
Why it matters: It reduces high-cost mistakes while still preserving workflow speed where risk is low.
Policy Enforcement Layer
When to use: When multiple models, tools, or services need consistent permissions, usage limits, and guardrails.
Why it matters: It keeps governance rules out of scattered business logic and makes changes safer to roll out.
Evaluation Before Automation
When to use: When teams want to automate a workflow but do not yet know what acceptable accuracy, reliability, or business quality look like.
Why it matters: It stops teams from scaling the wrong behavior and creates a measurable path to production readiness.
Common Failure Modes
- - Putting agent orchestration directly in a user request cycle with no queue, timeout strategy, or retry model.
- - Treating retrieval quality as a prompt issue instead of a data, ranking, and freshness architecture issue.
- - Adding human review only after an incident instead of designing approval and escalation into the system from day one.