phuongnz Design Agentic Architecture
Agentic complexity is a cost you pay down, not a default you start with. This skill turns a use case into a design document by climbing the ladder on evidence — adding structure only when a need forces it — on a foundation of durable state, wiring in memory, human-in-the-loop, and observability only where each earns its place.
The architecture rules are research-backed as of mid-2026 and live in PRINCIPLES.md — consult it as you design. Tools are checked live (step 7) because they age fast; never recommend them from memory. The output follows DESIGN-DOC-TEMPLATE.md.
Work the steps in order. Each ends on a completion criterion — do not advance until it is met.
1. Pin the use case
Capture the goal (one sentence), every action the agent can take (read vs. write/irreversible), the risk profile (irreversibility, blast radius, compliance), expected scale, latency/cost sensitivity, and the pricing regime (metered API vs. flat subscription — it sets how hard the cost arm pulls, and so how aggressively to optimize before climbing). If the goal is vague, interview the user before designing — a fuzzy use case yields an abstract, useless design.
Done when: you can state the goal in one sentence, have listed every action tagged read or write, and have captured the risk profile, expected scale, latency/cost sensitivity, and pricing regime.
2. Climb the ladder on evidence
Start at rung 1 (single agent + typed, validated tools). Add a rung only for an unmet need: correctness → reflection (a separate critic), structure → bounded planning, adaptivity → ReAct, hard org boundaries → multi-agent. Pick the lowest rung that meets the need. Before climbing, exhaust single-agent optimization — prompting, retrieval, caching, context engineering (curate/compact/offload), model upgrade; a window that is overflowing or rotting is a signal to fix the context first, not to add a rung. Multi-agent is the last resort (4–220× tokens, handoff latency, hard debugging) and only for boundaries — never for parallelism alone. If multi-agent, enforce the single-writer rule: one coordinator owns the plan and all writes; sub-agents return intelligence and full traces, not actions.
Done when: the chosen rung is named with the specific need that justifies it (or rung 1 is justified as sufficient), and single-agent optimization — including context engineering — has been exhausted before any climb.
3. Lay the durable-state foundation
Decide the checkpointing / durable-execution approach first — it is the shared substrate that memory, HITL, long runs, and replayable traces all stand on. Retrofitting it is painful. What travels through a checkpoint or handoff is usually a compacted form of the context (a summary, notes file, or handoff brief), not the raw transcript — persisting context and keeping it inside its budget (step 4) are one problem seen from two ends.
Done when: the design states how run state is persisted and resumed.
4. Place memory
Start in-context, but treat the window as a budget with a sweet spot — curate, compact (trim/summarise), and offload durable facts to memory before reaching for a bigger window or external memory. Add external memory only when token cost or window limits actually bite. Default to RAG/vector; reach for a knowledge graph only when relationships or change-over-time matter. Bigger context is not a memory strategy (long-context models rot and hallucinate on adversarial recall). Buy the write path if needed — but discount vendor benchmarks.
Done when: each kind of knowledge the agent needs is assigned to a layer (in-context or external) with a reason, and the context-budget plan (curate/compact/offload) is stated.
5. Place human-in-the-loop
Gate an action only where expected error cost > review-latency cost. Score each action on irreversibility, blast radius, compliance, and confidence — but never escalate on the model's raw confidence (LLMs are overconfident); combine trust + risk. Put gates at chain boundaries, few and async — never on every step, which manufactures rubber-stamping. Oversight is a per-action dial (in / on / out of the loop), not a global mode.
Done when: every write/high-risk action has an explicit gate decision, and routine actions are left out-of-the-loop.
6. Measure the middle
Agentic systems fail in the middle. Instrument on the OpenTelemetry GenAI conventions; evaluate the trajectory (tool-call accuracy, retrieval groundedness, handoff quality) at step level, not just the final answer; close the loop (production traces → eval datasets → CI). For multi-agent, attribute failures with MAST.
Done when: the design names what to trace and what to evaluate at step level, plus the end-to-end check.
7. Recommend tools — live
Tools age fast, so web-search current options per capability (orchestration, durable execution / HITL, memory, observability / evaluation) — do not list tools from memory. Present a landscape, not a ranking: the research found no verified head-to-head winner, and frameworks ship the same primitives. Flag the two evidence-backed anchors (OpenTelemetry, MAST). Always offer a build vs. buy option (assemble best-of-breed, or adopt one connected ecosystem). See PRINCIPLES.md for the build-vs-buy heuristics.
Done when: each capability lists at least one current tool, each carries the date its search was run, and the no-ranking caveat is stated.
8. Write the document and run the false-confidence check
Fill DESIGN-DOC-TEMPLATE.md and save it (default ./agentic-architecture-design.md unless the user names a path). Stamp it "architecture guidance as of mid-2026; tools searched ." Then run the false-confidence check: audit the design against the three traps in PRINCIPLES.md — the thing that looks fine while hiding a failure — and fix any you find before delivering.
Done when: the document exists at the path, is dated, and you have confirmed it contains none of the three false-confidence traps.