Agent Architecture Standards
These standards keep agents maintainable, swappable, and ready to scale from a single pilot into a portfolio of production workloads. They sit on top of the Modern AI Tech Stack and assume you have already chosen your foundation, brain, memory, and interface layers.
1. Core architectural guidelines
Modularity
Separate three concerns and never let them bleed together:
| Layer | Responsibility | Why it must be isolated |
|---|---|---|
| Agent logic | Instructions, planning, decisions. | You will swap models. The instructions outlive the model. |
| Tool integration | API clients, MCP servers, function definitions. | Tools change owners and auth schemes. Keep them behind interfaces. |
| Data access | Vector stores, databases, document loaders. | Data sources move. Schemas change. Caching, retries, and PII redaction live here. |
A modular layout is the only way to test pieces in isolation, swap a model without rewriting tools, or replace a vector store without touching agent instructions.
Integration standards
Define these once, organization-wide, before the second agent ships:
- Tool registry. A single source of truth for which tools exist, who owns them, and which agents may call them.
- Auth contract. Every tool exposes credentials the same way (e.g. service account, scoped token, OAuth). No agent stores raw secrets in its prompt.
- Schemas as the API. Tool inputs and outputs are typed (JSON Schema, Pydantic, Zod). The agent does not parse free-form prose from a tool.
- Idempotency keys for any tool that mutates state, so a retry never double-charges, double-emails, or double-creates a record.
- Versioned prompts. Treat the agent instruction set like code: source-controlled, peer-reviewed, with a change log.
2. From POC to production
Most agent projects die between the demo and the launch. The path that works:
| Stage | What it proves | Exit criteria |
|---|---|---|
| Spike | The model can do the task at all. | One end-to-end demo on real data. No production access. |
| Pilot | A real user gets value with HITL on every action. | 80%+ approval rate on a defined task set; baseline metrics captured. |
| Hardening | The agent is safe to leave running. | Eval suite (see Agent Evaluation), HITL where required (see Governance & HITL), security review (see Agent Security), monitoring in place. |
| Production | The agent earns its keep. | SLOs met for two consecutive review periods. |
| Iterate | Continuous improvement. | Weekly metric review; regression eval gates every prompt or tool change. |
Scalability
When you build the first agent, design as if there will be ten. That means:
- Shared infrastructure for prompts, tools, evaluation, and logging — not bespoke per agent.
- Per-agent budgets (tokens, dollars, rate limits) so a runaway loop never takes down the system.
- Separate
dev/staging/prodenvironments with realistic but non-PII data in non-prod. - Capacity tested under load — concurrent conversations, long-context messages, slow tool responses.
3. Performance monitoring
What to instrument from day one:
| Metric | Why it matters |
|---|---|
| Latency (p50 / p95 / p99) per agent and per tool | Slow tools poison the user experience faster than wrong answers. |
| Token usage in / out per request | Direct cost signal; spikes mean prompt drift or runaway loops. |
| Tool call count and failure rate | Agents stuck in loops show up here first. |
| Approval / rejection rate (HITL) | The honest measure of trust. Rising rejection rate = something regressed. |
| Eval score (rolling, on the golden set) | Catches regressions from prompt or model changes. |
| User-reported issues | The ground truth your dashboards will miss. |
Wire each metric to an alert threshold before launch, not after the first incident.
4. The architecture review checklist
Before promoting any agent to production, confirm:
- Agent logic, tool integration, and data access are separated.
- All tools live in the registry with named owners.
- Every mutating tool has an idempotency strategy.
- Prompts are version-controlled and reviewed.
- Per-agent budget limits are enforced.
- An eval suite gates prompt and model changes.
- HITL is wired in for any irreversible action (see Governance & HITL).
- Latency, cost, tool-failure, and rejection-rate metrics are emitting.
- Alerts fire on threshold breaches.
- A rollback path exists — previous prompt and previous model are one toggle away.
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