AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
Agent Advisor provides a structured, multi-phase workflow to help users determine the optimal AWS runtime for their AI agents. It uses a deterministic scoring engine to evaluate requirements and guides users through a process that includes discovery, architectural design, cost estimation, and the creation of an executable proof-of-concept. The skill supports various AWS compute options, including AgentCore, ECS, EKS, and Lambda, and integrates with migration engines for existing workloads.
What it does
- Recommends AWS runtimes for AI agents based on technical requirements.
- Generates migration plans for existing AI workloads moving to AWS.
- Provides architectural guidance for agentic systems.
- Assembles executable proof-of-concept scaffolding.
- Estimates coarse cost magnitudes for proposed agent deployments.
- Integrates with external migration engines for workload transitions.
When to use it
- When you need to decide which AWS runtime is best for a new AI agent.
- When migrating an existing AI agent workload to AWS.
- When you need to add services like memory, identity, or observability to an existing AWS agent.
- When you require a structured plan and POC for deploying agents on AWS.
Advantages
- Provides a deterministic, repeatable recommendation process.
- Supports a wide range of AWS compute environments.
- Offers a unified flow from initial idea to executable POC.
- Adapts to both technical and business-focused user backgrounds.
Usage examples
- “I have an agent idea, what do I build?”
- The agent initiates the intake process to clarify your requirements and recommend an appropriate AWS architecture.
- “Move my agents to AWS.”
- The agent triggers the migration workflow to assess your existing workload and plan the transition to an AWS runtime.
- “Which runtime for my agent, AgentCore vs ECS vs EKS vs Lambda?”
- The agent runs a scoring analysis to compare these runtimes against your specific agentic needs.
- “Add AgentCore services to an agent already on AWS.”
- The agent enters the add-capabilities branch to guide you through integrating memory, gateway, and observability services.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/aws/agent-advisor/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip agent-advisor.zip -d .agents/skills
Documentation by AWS
AWS Agent Advisor
Helps startups decide how and where to run AI agents on AWS. Deterministic scoring recommends a runtime; the conversation adapts to the user's technical background.
Definitions
"Load" = Read the file with the Read tool and follow it. Do not summarize or skip.
$RUN_DIR= the run directory under.agent-advisor/(e.g..agent-advisor/0630-1430/), created in Intake.$PLUGIN=${CLAUDE_PLUGIN_ROOT}(the installed plugin root). On Claude Code this token substitutes inline. If${CLAUDE_PLUGIN_ROOT}does not resolve (some Cursor/Codex builds, or a literal${CLAUDE_PLUGIN_ROOT}string showing up in a path error), fall back to the skill's own directory: this SKILL.md lives at<plugin>/skills/agent-advisor/SKILL.md, so the engine and its data are all inside this skill — scripts at./scripts/..., runtime profiles at./references/runtimes/..., and decision refs at./references/decision-refs/...relative to it. Prefer${CLAUDE_PLUGIN_ROOT}/skills/agent-advisor/...; use the relative fallback only when it fails to resolve.
Prerequisites
uvavailable (for scoring). Check:uv --version. If missing, tell the user to install it from the official install guide (https://docs.astral.sh/uv/getting-started/installation/ — e.g.brew install uvorpipx install uv) and stop.
Phase Structure (frontmatter)
Phase, fragment, and assembler files carry a YAML frontmatter block that declares how each
phase is composed — its inputs, triggers, fragments, assembler, artifacts, gates, and
ordering. The execution contract is the vendored references/vendored/dsl/INTERPRETER.md:
it defines every frontmatter key, the fragment/assembler model, the gate protocol
(HANDOFF_OK / GATE_FAIL), and the interpreter loop. Load it first (once, at the
start of a run), then execute each phase file's prose body. Elsewhere in this skill,
INTERPRETER.md (without a path) refers to this loaded contract.
Execution
This skill is driven by the interpreter loop in INTERPRETER.md (§ The interpreter loop):
it reads .phase-status.json, determines the current phase, runs each phase's
_preconditions / fragments / _assemble / _postconditions, advances on HANDOFF_OK
via _advances_to, and validates state. The backbone (intake → discover → clarify →
confirm → design → estimate → generate → migration-plan → poc → complete) and the
one sidebar branch (add-capabilities) are derived
from the phase files' frontmatter — they are not restated here.
Cold start (entry phase). With no run under .agent-advisor/ carrying a
.phase-status.json, begin at references/phases/intake/intake.md — this skill's entry
phase (the one carrying _init: true). On a warm start, current_phase in
.phase-status.json is authoritative (INTERPRETER.md § The interpreter loop).
Skill bindings (INTERPRETER.md § Skill bindings). This skill declares:
Run root:
.agent-advisor/—$RUN_DIRis this skill's name for the run directory (.agent-advisor/[MMDD-HHMM]/). Intake's own prose performs the_initbootstrap.State shape: § State file below (advisor-specific keys such as
entry_point,audience,recommendation_reviewed,migration_plan_ctx,migration_plan_unavailable); the shared state schema is not vendored.Run seed (optional):
$RUN_DIR/seed.json, else.agent-advisor/seed.jsonat the run root (schemascripts/schemas/seed.json) supplies machine-readable answers for a non-interactive run — the Clarify dimensions, the two gate answers, the POC mode, the live-probe answer, and aco_recommendtie-break. It is the HIGHEST-precedence source for every value it carries (clarify.md Step 2.5), which is what makes a repeated run's score comparable: the deterministic engine gets byte-identical input. A gate the seed omits is declined; a dimension the seed omits falls through to detection, then prose, then anassumedvalue that MUST be recorded in$RUN_DIR/UNANSWERED.md. With no seed, the interactive flow is unchanged.Resolved statuses:
skipped(routing resolved the phase without running it), plusnot_applicableformigration_planonly.Conditional backbone routing: the entry-point routing below. When a routing rule marks a phase not-applicable, set it
skippedand advance through its_advances_toin the same state write.
Routing & gates (orchestration)
Sidebar placement and conditional backbone routing are orchestration prose owned by
this file (INTERPRETER.md § Skill bindings, § Backbone vs sidebar).
Entry-point routing:
build_scratch→ skip Discover; Clarify → Confirm → Design → Estimate → Generate → Gate 2 → POC (any winning runtime). No migration plan (nothing existing to migrate).build_deploy→ Discover (if code) → Clarify → Confirm → Design → Estimate → Generate → Gate 1 → Migration Plan (if existing non-AWS AI workload detected and user confirms) → Gate 2 → POC (any winning runtime).migrate→ Discover (if code) → Clarify → Confirm → Design → Estimate (target-state run cost; migration TCO comparison stays with the Migration Plan engine) → Generate → Gate 1 → Migration Plan (in-skill, reusing the siblinggcp-to-awsskill) → Gate 2 → POC (any winning runtime, when the plan was produced). Declining Gate 1 keeps the classic handoff: pointer to/aws-startup-advisor:llm-to-bedrockwithhandoff-summary.md.add_capabilities→ loadreferences/phases/add-capabilities/add-capabilities.mdand follow it (no runtime scoring; writescapabilities-recommendation.md). This is a self-contained branch — it does NOT pass through Clarify / Confirm / Design / Estimate / Generate, so the phase gate below never applies to it.Temporal detection routes into
migratewith temporal units pre-seeded (see discover).
Gate semantics (backbone tail):
Gate 1 →
migration_planruns only whengenerateis done ANDrecommendation_reviewed == true(generate.md Step 5.5) AND entry point ∈ {migrate, build_deploy} AND the run is migration-eligible (generate.md Step 6) AND the user confirmed Gate 1. Otherwise resolve it:not_applicable(build_scratch / no migratable workload) orskipped(declined) — and advance.Gate 2 →
pocruns only whenphases.poc == "in_progress"(set when the user answers Gate 2 "yes" — asked in generate.md Step 7 or migration-plan.md Step 6) ANDrecommendation_reviewed == true. Any winning runtime (agentcore / ecs / eks / lambda / lambda_microvms) — the POC shape follows the verdict (poc.md Step 3 dispatch onreferences/decision-refs/poc-shapes.md). Gate 2 is only offered whenmigration_plan∈ {completed, skipped, not_applicable} — orin_progresson build_deploy only (Stage 2 failed/aborted; fallback POC from design.json per migration-plan.md failure handling); for entry pointmigrate, only whenmigration_plan == "completed"(the POC implements the plan) OR when the stage resolvednot_applicablewithmigration_plan_unavailable == "engine_absent"— a standalone deployment that does not bundle the migration engine, where Gate 2 is offered by migration-plan.md Step -1 and the POC is design-backed. A migrate-POC with no plan for any OTHER reason (the user declined) has nothing to implement.Persisting Gate 2 as
phases.poc = "in_progress"BEFORE poc.md loads makes the confirmation resumable: if the session breaks between the "yes" and the load, the interpreter re-enterspocwithout re-asking. (A declared deviation fromINTERPRETER.md§ The interpreter loop step 5's gate-then-in_progressordering — the user's confirmation is the entry event worth persisting.)
Phase gate: Do NOT load design.md / estimate.md / generate.md unless
$RUN_DIR/.phase-status.json exists and BOTH phases.clarify == "completed" AND
phases.confirm == "completed". Confirm confirms the deployment model, the service
set, and (for a co_recommend tie) the user's chosen_runtime — Design and the diagram depend on
its confirm.json output, so it must not be skipped. If the user asks to skip Clarify or Pass 2,
refuse briefly and run it.
State file (.phase-status.json)
{
"run_id": "0630-1430",
"entry_point": "build_scratch",
"audience": "technical",
"current_phase": "clarify",
"phases": {
"intake": "completed",
"discover": "skipped",
"clarify": "in_progress",
"confirm": "pending",
"design": "pending",
"estimate": "pending",
"generate": "pending",
"migration_plan": "pending",
"poc": "pending"
}
}Status values: pending → in_progress → completed, plus skipped. Use read-merge-write:
read before each update, change only the advancing keys, keep prior phases.
recommendation_reviewed (top level, boolean) is set to true by generate.md Step 5.5 when
the user explicitly confirms they have seen the recommendation. Gate 1, Gate 2, and the
migration_plan / poc states all require it — no gate may be asked while it is absent.
migration_plan additionally uses not_applicable (build_scratch, or no migratable workload
detected). When Stage 2 runs, migration_plan_ctx is added at the top level:
{"repo": "<abs path to target repo>", "migration_dir": "<abs path to .migration/<id>/>"} —
Stage 3 reads gcp-to-aws artifacts ONLY via this recorded path, never by re-globbing.
Files
| File | Purpose |
|---|---|
references/vendored/dsl/INTERPRETER.md | Vendored DSL execution contract (interpreter loop + gate protocol) |
references/phases/intake/intake.md | Entry point + technical background + open context |
references/phases/discover/discover.md | Lightweight code detection |
references/phases/clarify/clarify.md | Clarify orchestrator + answer mapping to scoring keys |
references/phases/clarify/clarify-technical.md | Technical-background question wording |
references/phases/clarify/clarify-business.md | Business-background question wording |
references/phases/confirm/confirm.md | Winner-specific follow-ups |
references/phases/design/design.md | Assemble recommendation; Migrate handoff branch |
references/phases/estimate/estimate.md | Coarse cost magnitude |
references/phases/generate/generate.md | Layered recommendation doc + scaffolding |
references/phases/migration-plan/migration-plan.md | Stage 2: full migration plan via the sibling gcp-to-aws engine |
references/decision-refs/temporal.md | Temporal rules: Tier 1/2 tables, adapter, runbooks, commercials (consumed by discover/clarify/design/generate) |
references/decision-refs/poc-shapes.md | Per-runtime POC deploy shapes (ECS/EKS/Lambda/MicroVMs/Temporal) |
references/decision-refs/*.md | Runtime service cards, model defaults, freshness |
references/decision-refs/workload-classes.md | Deterministic verdicts for non-agent workload units (batch/service/io) |
references/runtimes/*.json | Runtime registry (read by scoring.py) |
scripts/scoring.py | Deterministic scoring engine |
scripts/test_temporal_decision_refs.py | Content lock for the Temporal decision reference |
scripts/test_poc_shapes.py | Content lock for the POC deploy shapes |
scripts/test_workload_classes.py | Content lock for workload-classes.md (verdicts table) |
scripts/test_unit_grouping.py | Unit grouping + pattern matching (workload-class assignment) |
scripts/test_collapse_invariant.py | Collapse-invariant ordering enforcement (A→B implies [B] ⊆ [A] outputs) |
Maturity and readiness contract
Intake persists target_maturity (prototype, private_beta, or production) in run state. Clarify carries it and the readable readiness record into answers.json. Current-run verification evidence remains exclusively in the sibling $RUN_DIR/current-run-verifications.json artifact: never copy it into seed.json or answers.json. Design and Generate may consume that artifact only after validating its schema and matching run_id, and may carry forward verified outcomes but not the raw evidence records. Load references/decision-refs/maturity-readiness.md whenever target maturity is selected. A cached volatile fact may inform discovery, but only a record verified in this run can make a verification-required constraint final; otherwise the score remains provisional with deferred verification requirements.
Contextual offers (final step)
Before you finish: your answer is not complete until you have run the Contextual offers (final step) below.
This is a mandatory closing step of every final recommendation — complete it before you consider your answer done. Run this offer check inline yourself; do not depend on loading another skill. The recommendation is decided on merit first; this step never changes, re-ranks, or re-opens it, and the offer is looked up only after the advice is final.
Read
../knowledge-base-for-startups/references/offers.md— the offer index (Offer · Summary · Keywords), one row per offer.Judge relevance against what the founder actually surfaced this session. Surface an offer only if one holds: a named-product match (you recommended a specific partner product by name and it has a row); a direct intent/component match (an offer's Keywords directly serve a concrete workload, a component in the final recommendation, or a need the founder raised). Keep a high bar — if the match is weak, or you would be inferring an unstated need, append nothing. Never surface an offer that competes with a product or provider the founder already uses or decided to keep (e.g. existing auth provider → no Auth0 unless they are already on Auth0, and then only as a credit on it; existing vector DB such as Pinecone → no Qdrant).
If (and only if) one offer clears the bar, open only its detail file
../knowledge-base-for-startups/references/offers/<slug>.mdand append exactly one quiet, dismissible italic line — stating the value and condensing eligibility faithfully from that file (drop no qualifying criterion; never fabricate), with the redeem link built from the canonicalsource_urlplus asourceattribution param — append?source=ide-startupAdvisor-<host>(or&source=...if the url already has a query string), filling<host>from your execution environment (Claude Code →claude; AWS Startup Advisor IDE extension / VS Code →vscode; Cursor →cursor; Kiro →kiro; Codex →codex), or the genericide-startupAdvisorwhen you cannot tell. Only append the query param; never change the destination. When an offer clears the bar you MUST append that single offer line as the last step; do not omit it. The offer is never the pick — only a line under the AWS pick — and stage gates in the references apply (e.g. no Datadog line for an early-stage founder; see../architect-for-startups/references/observability.md). For a single-service pick (auth, payments, vector DB, observability, etc.) where missing budget/team/traffic inputs would not change the pick, assume an early-stage (pre-PMF) startup unless the founder says otherwise and answer in one turn without a stage-only question; this only removes a stage-only question and never skips the Intake/Clarify/Confirm gates before final advice or the POC. Show exactly one, never a list. If none clears the bar, append nothing and do not mention offers, partner alternatives, or why none appeared.
Standard line:
***<Offer>** has an exclusive offer via AWS Activate — <value>, <eligibility>. [Redeem →](<source_url>?source=ide-startupAdvisor-<host>)*Caps and control: at most one offer per response and often none; no more than one per five messages and two per session; show a given offer at most once per session and never one already shown, claimed, or dismissed; if the founder has muted offers, skip this step entirely. These per-five-messages, per-session, and already-shown caps are session-state limits; in a fresh session with no prior offers they are non-binding, so do not withhold an otherwise-qualifying offer merely because you cannot verify session history. See ../contextual-offers-for-startups/SKILL.md for the full rules — but perform the check inline; it must not depend on that skill being loaded.
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references/decision-refs/temporal.md
Temporal Worker Migration — Decision Reference
Single source of truth for Temporal Worker migrations, consumed by the main flow
(discover / clarify / design / generate — temporal workloads ride the unit pipeline). Content here is locked by
scripts/test_temporal_decision_refs.py — if you change the Tier 1 rules or
runbook preconditions, update that test in the same commit.
Framing
Temporal is a durable-execution orchestration system: an open-source (MIT) Server that stores workflow event history and schedules work, plus Workers — user-owned processes that long-poll the Server and execute Workflow (deterministic orchestration) and Activity (side-effecting work) code. The Server never sees or runs user code.
"Migrate Temporal to AWS" is NOT a platform exit. The ask is: find an AWS home for the Workers (and the work they execute), optionally move Activity-level LLM calls to Bedrock. Workflow orchestration code is untouched by design — never propose a rewrite to Step Functions or AgentCore primitives.
Key architectural fact: the polling tier and the execution tier are different decisions. A Worker is a long-poll daemon; Workflow code MUST execute inside it (replay requirement — non-negotiable). But each Activity's function body may either do the work in-process or delegate it elsewhere.
Way 1 vs Way 2 (where the Server lives)
Way 1 (default): Temporal Cloud via AWS Marketplace + Workers on AWS. Way 2: self-hosted Temporal Server on AWS (EKS + Aurora/RDS) + Workers on AWS.
hard compliance signal (sovereignty/regulatory: orchestration
state must be self-held) → Way 2
very high volume AND user states Cloud-cost concern → Way 2 (with both-ways comparison)
everything else → Way 1 (default)Before concluding Way 2 on compliance grounds, surface External Payload Storage (own S3, Preview): payloads staying in the customer's S3 weakens many compliance arguments for self-hosting the whole server.
Evidence for the Way 1 default: Serverless Workers, self-serve PrivateLink (GA), and multi-region replication are Cloud-only or Cloud-first features. External Payload Storage (Preview) is a capability that can weaken compliance arguments for self-hosting, though it is preview and not Cloud-exclusive.
Tier 1 — Polling tier (where the Worker daemon lives)
Every rule is decisive (eliminate or direct-pick); no weighted trade-offs, no scoring script.
Eliminations
Lambda classic: 15-min cap + freeze model is incompatible with a long-poll loop → OUT.
AgentCore Runtime: 8-hour max execution → OUT as Worker host. (Its correct role is the execution tier for agent-session Activities — Tier 2.)
Lambda MicroVMs: can run long, but resident-polling cost is strictly worse than ECS → OUT.
Rules (evaluate IN ORDER; first match wins)
1. K8s AND low/spiky (the only genuine tension) → present both EKS and
Serverless Workers to the user
(AskUserQuestion), don't auto-pick
2. team already operates K8s → EKS
3. low/spiky traffic AND all Activities < 15 min
AND Way 1 AND accepts Public Preview status → Lambda Serverless Workers
(label Public Preview, always)
4. mixed durations → OFFER a split (AskUserQuestion):
split task queues (short →
Serverless Workers, long → small
ECS service) vs one plain ECS
service for everything. The split
buys scale-to-zero on the short
side but adds a second deployment
unit — declining it is legitimate.
5. otherwise → ECS Fargate (default)The tension rule is first so an EKS direct-pick cannot pre-empt it. Rules 1 and 4 are user-choice gates, not direct picks; the OTHER rules are decisive. When the user declines rule 4's split, the outcome is recorded as "rule 4 offered, user chose single ECS service" — NOT as falling through to rule 5. Apply the rules per task queue when the inventory shows several.
Tier 2 — Execution tier (what each Activity delegates to)
Classify each Activity (scan + user confirmation), then map:
| Activity class | Signature | Execution target |
|---|---|---|
| Light-IO (single LLM/API call, DB read-write) | IO-bound, seconds–minutes | in-process — delegation adds hops for nothing. In-process does NOT waive Temporal hygiene: idempotency, Activity timeout/retry options, cancellation handling, heartbeats for external calls |
| Agent-session (LLM loop + tools + memory, tens of minutes) | stateful session, isolation, independent scaling | run scoring.py via the adapter table below (§ Tier 2 adapter — agent-session Activity classes); typical winner AgentCore Runtime (microVMs ≤8h; Instances up to 14d for longer Activities) — DAF pattern; community reference github.com/koukish/async-agent-architecture-sample (not official) |
| Short-tool (convert, scrape, validate) | stateless, seconds, spiky | Lambda (<15 min) |
| Heavy (batch, fine-tune, >15 min, high CPU/GPU) | long, resource-dense | ECS task / AWS Batch; GPU → EC2 |
Tier 2 adapter — agent-session Activity classes
For agent-session Activities, build the scoring.py input via this adapter (unlisted dimensions stay "unknown" — the engine's DEFAULTS apply):
| scoring.py dimension | Fill from Temporal context |
|---|---|
session_duration | max expected runtime of this Activity (under_15min / 15min_to_8hr / over_8hr) |
traffic_pattern | Activity start pattern from workflow schedule/volume (bursty / steady / idle) |
launch_concurrency | expected concurrent Activity starts (high / moderate / low) |
session_state | agent loop holds conversation/tool state → stateful; waits for human signal → hitl |
memory_needs | memory scoped to one Activity run → session_only; persists across runs → cross_session. A retried/re-dispatched agent Activity (e.g. re-draft after a rejection signal) counts as a NEW run — stay session_only unless the business logic needs memory to carry across executions (shared investigation context, cross-ticket recall) |
isolation | per-session sandbox needed (untrusted code/tools) → required |
framework | detected agent framework in the Activity body (strands / langgraph / crewai / custom / none) |
platform_fit | caller is an in-VPC Worker, not a user-facing endpoint → team's existing AWS fit if stated (ecs / eks), else none |
existing_cluster | from Tier 1 answer (team's K8s/ECS reality) |
ops_preference, multi_agent, compute_tier, others | ask in Step 2 only if load-bearing; otherwise unknown |
Cutover runbooks
Runbook 1 — Server unchanged: zero downtime for Workflows
Applies when the Server stays where it is (Temporal Cloud, or an untouched self-hosted cluster) and only Workers move to AWS.
New workers (AWS) join the same namespace + task queues → verify task pickup → drain old workers. Replay reconstructs in-flight Workflow state on new workers; no pause needed.
Preconditions (state these explicitly in every generated plan):
New workers register the SAME workflow and activity types (compatible code).
Same task queue names and namespace; mTLS certs / API keys provisioned on the AWS side before cutover.
Old workers shut down GRACEFULLY (finish or heartbeat-cancel running Activities). Currently-executing Activities do NOT migrate mid-execution: they complete on the old worker, or fail and rely on the Activity retry policy to be re-dispatched to a new worker. Verify retry policies + heartbeat timeouts before drain.
Runbook 2 — Self-hosted → Temporal Cloud: drain/dual-run
New executions start on Cloud; old executions finish on the old cluster, then decommission. WARN loudly: there is no official cross-cluster history migration tool. Detect in-flight long-running workflows during the scan and set expectations — a workflow with months to run keeps the old cluster alive for months. Interruptible flows may checkpoint business state and restart on Cloud.
Runbook 3 — Workflow-determinism-affecting code changes (e.g. Bedrock rewrite)
The replay constraint is broader than Activity signatures: Workflow code must stay deterministic against recorded history — including Activity type/name and arguments at already-recorded command points. Options:
keep changes replay-compatible, OR
isolate via Worker Versioning (GA) or a new task queue.
Worker Versioning routes existing executions to compatible Build IDs — it is NOT a compatibility layer: old Build IDs / old task queues must keep serving workers until their histories drain.
Temporal Cloud commercials (for the plan's commercials section)
What this section contains depends on the CURRENT server state, not just the chosen Way:
New Temporal Cloud customer (current state: self-hosted server, chose Way 1) — the full subscribe flow applies:
AWS Marketplace SaaS listing
prodview-xx2x66m6fp2lo; $0.01/action, pay-as-you-go; free trial + $1,000 credits; 12+ AWS regions; SCMP contract; Vendor Insights available.Flow: Marketplace subscribe → account link → create namespace → billing lands on the AWS bill (EDP-eligible per agreement).
Self-serve PrivateLink: GA.
Already on Temporal Cloud (current states 2/3/5) — do NOT pitch the subscribe flow; they already have a namespace and pay for it. Instead:
Billing and namespace unchanged by this migration — only the workers move.
Self-serve PrivateLink (GA): connect the new AWS-side workers to the existing namespace via a VPC endpoint.
Optional commercial note: if they currently bill directly with Temporal, the AWS Marketplace listing may allow moving billing onto the AWS bill (EDP-eligible) — a question for their Temporal account team, not a technical migration step. Do not present it as required or guaranteed.
Way 2 (self-hosted on AWS) — no Cloud commercials at all; the plan carries the self-host stack (EKS + Aurora/RDS) and its ops cost framing instead.
Serverless Workers (⚠️ Public Preview — label it, always)
Status: Public Preview (docs.temporal.io currently reads "AWS Lambda — Public Preview", verified 2026-08). Do not trust the docs label at face value — it has shifted before without a GA announcement (it read "Available" in 2026-07 while the feature was still pre-release); re-verify at generation time and label the output Public Preview until GA evidence appears (see freshness.md, Temporal section).
Mechanism: Temporal Cloud invokes Lambda on task arrival (no persistent poll); fresh connection per invocation; Workflows span invocations via replay — a 3-month workflow becomes hundreds of discrete invocations with zero cost while waiting. Activities hard-capped at 15 min.
Not for: >15-min Activities, persistent-connection features, sustained high throughput (resident ECS wins on cost at sustained load).
Cloud-only (Way 1). Per-SDK/language support matrix: verify at generation time (docs currently only confirm a Go guide).
Replay 2026 feature statuses (cite with labels)
| Feature | Status |
|---|---|
| Workflow Streams | Preview |
| External Payload Storage (own S3) | Preview |
| Worker Versioning | GA |
| Task Queue Priority/Fairness | GA |
| OpenAI Agents SDK integration | GA |
| Google ADK integration | Available |
| Multi-region/multi-cloud replication | GA (RTO 20 min) |
| Self-serve PrivateLink | GA |
| AWS AI Competency | Agentic AI (badge) |
If the user asks "why not Step Functions?"
The generated plan does NOT include a Step Functions comparison section. If the
user raises it in conversation, answer briefly from these facts and move on:
first-class signals/HITL vs waitForTaskToken plumbing; unlimited execution
duration vs the 1-year Standard cap; workflows as ordinary code with
time-skipping unit tests; SFN per-transition pricing is far cheaper per step
but that is an engine-fee-only comparison — the rewrite cost and capability
loss dominate. Do not volunteer this unprompted.
Requirements and compatibility
Written for: Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Names Claude Code tools, Needs Python
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