What happens when your AI agent fails halfway through a critical multi-step task? I dug into three serious saga solutions (Temporal, LangGraph, and Axon) to see how they actually handle failure recovery in agentic workflows. The differences are bigger than I expected, and they matter a lot depending on what you're bui...
Most of the conversation around sagas lives in the microservices world, where the pattern has been well-understood since at least the Pat Helland days, but AI agents introduce a different flavor of the problem. Your steps are slower (LLM calls can take 10-30 seconds each), way more expensive to retry blindly, and the failure modes are weirder. A step can "succeed" in the HTTP sense but return garbage that breaks the next step. Or it can succeed, then the agent gets killed mid-flight, and you have no idea what got committed downstream. I've experimented building agent workflows with all three of these tools: Temporal, LangGraph, and Axon Framework. Here's what I actually found, with real agent code for each one. The Problem Space: Why Agents Need Saga Logic An AI agent that just calls one LLM and returns a result doesn't need any of this. But real agentic systems don't look like that. They look more like this: Retrieve context from a vector store Call GPT-4o to plan sub-tasks For each sub-task, call a specialized agent or tool Write intermediate results to a database Call another model to synthesize the final output Notify downstream systems Any step can fail. Some failures are retriable (transient network blip). Some aren't (the context you retrieved is now stale and you need to start over). And some require compensation: if step 4 wrote to the database and step 5 failed, you probably need to roll that back or mark it as invalid. That's a saga. Distributed steps, partial failures, compensation actions. Temporal: The Workflow Engine That Actually Gets It I'll just say it: Temporal is my default recommendation for production agentic systems. It was designed exactly for this problem, even if the AI agent use case is newer than the codebase. The core idea is that you write workflows as regular code, and Temporal handles durability through event sourcing on the server side. Your workflow code can sleep for days, survive process crashes, and replay deterministically. For an agent that might be waiting on a human approval or a slow model call, this matters a lot. Temporal has both Python and Java SDKs, and the Java one is particularly mature. Let's look at a real AI agent implementation. The activities are where your actual LLM calls live: @ActivityInterface public interface AgentActivities { List<String> retrieveContext(String query); AgentPlan planSubTasks(List<String> context); String executeSubTask(String subTask, List<String> context); String synthesizeResults(List<String> subTaskResults); void compensateResults(String recordId); String writeIntermediateResults(AgentPlan plan); } @Component public class AgentActivitiesImpl implements AgentActivities { private final OpenAiClient openAiClient; private final VectorStoreClient vectorStore; private final ResultRepository resultRepository; @Override public List<String> retrieveContext(String query) { return vectorStore.similaritySearch(query, 5); } @Override public AgentPlan planSubTasks(List<String> context) { String prompt = """ Given this context, break the task into sub-tasks: %s Return a JSON list of sub-task descriptions. """.formatted(String.join("\n", context)); String response = openAiClient.chat( ChatRequest.builder() .model("gpt-4o") .message("system", "You are a planning agent.") .message("user", prompt) .build() ); return AgentPlan.fromJson(response); } @Override public String executeSubTask(String subTask, List<String> context) { String prompt = """ Execute this sub-task using the provided context: Sub-task: %s Context: %s """.formatted(subTask, String.join("\n", context)); return openAiClient.chat( ChatRequest.builder() .model("gpt-4o-mini") .message("user", prompt) .build() ); } @Override public String synthesizeResults(List<String> subTaskResults) { String combined = String.join("\n---\n", subTaskResults); return openAiClient.chat( ChatRequest.builder() .model("gpt-4o") .message("system", "You are a synthesis agent. Combine these results coherently.") .message("user", combined) .build() ); } @Override public String writeIntermediateResults(AgentPlan plan) { return resultRepository.save(plan).getId(); } @Override public void compensateResults(String recordId) { resultRepository.markVoid(recordId); } } Now the workflow, which is where the saga logic lives: @WorkflowImpl public class DocumentAgentWorkflowImpl implements DocumentAgentWorkflow { private final AgentActivities activities = Workflow.newActivityStub( AgentActivities.class, ActivityOptions.newBuilder() .setStartToCloseTimeout(Duration.ofSeconds(60)) .setRetryOptions(RetryOptions.newBuilder() .setMaximumAttempts(3) .setInitialInterval(Duration.ofSeconds(2)) .setBackoffCoefficient(2.0) .setDoNotRetry(NonRetryableAgentException.class.getName()) .build()) .build() ); @Override public String process(String query) { String recordId = null; try { // Step 1: retrieve context from vector store List<String> context = activities.retrieveContext(query); // Step 2: planning agent breaks the task down AgentPlan plan = activities.planSubTasks(context); recordId = activities.writeIntermediateResults(plan); // Step 3: execute each sub-task in parallel List<Promise<String>> subTaskPromises = plan.getSubTasks().stream() .map(subTask -> Async.function(activities::executeSubTask, subTask, context)) .toList(); List<String> subTaskResults = subTaskPromises.stream() .map(Promise::get) .toList(); // Step 4: synthesize agent combines everything return activities.synthesizeResults(subTaskResults); } catch (Exception e) { if (recordId != null) { activities.compensateResults(recordId); } throw e; } } } The Async.function call on step 3 is something I really like about Temporal. Sub-tasks run in parallel, but the workflow still replays correctly if anything crashes mid-flight. And setDoNotRetry(NonRetryableAgentException.class.getName()) lets you mark certain failures (like a context staleness error) as non-retriable, so Temporal goes straight to compensation instead of burning through retries. One determinism rule worth remembering: you can't call UUID.randomUUID() or System.currentTimeMillis() directly in workflow code. Use Workflow.randomUUID() and Workflow.currentTimeMillis() instead. Forget that once and you'll spend an afternoon debugging a replay mismatch. Ask me how I know. LangGraph: The Right Tool for the Wrong Reason LangGraph came onto my radar when I was building a research assistant agent last fall, mostly because we were already deep in the LangChain ecosystem. The pitch is compelling: model your agent as a state graph, where nodes are steps and edges are transitions, and get checkpointing and some retry logic kind of for free. LangGraph is Python-only, so the code here is Python. If your team is JVM-first, LangChain4j exists and is improving, but it doesn't have a graph model equivalent yet. Here's what a real multi-step AI agent looks like in LangGraph, with actual LLM calls and compensation routing: from langgraph.graph import StateGraph, END from langchain_openai import ChatOpenAI from langchain_core...