AXME CLOUD
Write agent workflows without determinism constraints — AXME handles replay differently
Temporal's determinism requirement breaks the moment you introduce an LLM call — you can't call OpenAI inside a Workflow function. This is the #1 adoption blocker for AI teams.
Unlike Temporal, AXME's intent lifecycle model doesn't require deterministic code.
Agents are not replayable microservices
Temporal and similar engines assume workflow code is deterministic so the platform can replay history. The moment you call an LLM, use randomness, or depend on live external APIs, that model breaks — teams split logic into Activities, sagas, and workarounds.
AXME tracks state at the intent lifecycle boundary. Non-deterministic code runs inside your agent as usual; durability comes from persisted intent state, not from re-executing every line of Python.
CAPABILITIES
How it works.
Intent-level state
Replay at lifecycle boundaries, not every line.
LLM-friendly
Non-deterministic code is first-class.
External I/O
APIs, randomness, humans — no workaround layers.
LLM calls inside workflows
Temporal
# Cannot call OpenAI inside Workflow fn # Must use Activities + strict replay rules
AXME
intent = axme.submit(agent_step) result = await openai.chat(...) # OK await intent.complete(result)
When Temporal still wins
Mature microservice orchestration with existing Temporal expertise and purely deterministic workflows.
When to choose AXME vs Temporal
Choose AXME when agent steps include LLMs, human gates, and heterogeneous tools in one flow. Temporal remains strong for mature deterministic microservice orchestration where teams already invested in worker fleets and replay discipline.
Migration paths often run AXME for new agent features while legacy Temporal workflows wind down — see /migrate/from-temporal/ for concept mapping.
Common questions
- Can I still use idempotent tool calls?
- Yes — idempotency at the tool layer is good practice. AXME does not require deterministic functions; it requires durable intent state.
- How are failures recovered?
- Failed intents retain context for retry, human escalation, or inspection — without replaying the entire workflow from line one.
- Is randomness in agent code allowed?
- Yes — random seeds, sampling, and LLM temperature do not violate the execution model.
Related reading
Deeper dives from the AXME blog.
What Temporal Can't Do: Human Approval Mid-Workflow
Temporal gives you durable execution. But adding a human approval step mid-workflow requires building a signal handler, notification service, and UI. There's a simpler way.
Read post →Temporal Alternative Without the Cluster and Determinism Constraints
Temporal is the go-to for durable execution. But it requires a cluster and forces determinism constraints on your workflow code. Here's an alternative that gives you durability through a managed API with none of those trade-offs.
Read post →
Related
Related links
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