Vektor/Blog/Multi-Agent Workflows
Technical · 8 min read

Designing Reliable Multi-Agent Workflows

Multi-agent systems amplify both capabilities and failure modes. After watching billions of traces flow through Vektor, three patterns of failure keep showing up — and three corresponding evaluation patterns catch them before they reach production. The taxonomy below owes a lot to Anthropic's research on building effective agents, which separates orchestration patterns cleanly enough that you can reason about each one in isolation.

A
Aryan Mehta
Jan 28, 2025 · CEO, Vektor

When a single agent fails, debugging is usually straightforward — you replay the trace, find the bad tool call, and fix the prompt. When a workflow of five agents fails, the failure may have originated three agents upstream, been silently compounded by two more, and only surfaced at the final output.

1. Handoff validation

The single most common failure we see is a malformed handoff — an orchestrator agent confidently emits a payload that the downstream agent silently fails to parse, and the error doesn't surface for another two or three hops. Vektor's handoff evaluator treats every inter-agent message as an API call and validates it against the declared input schema of the receiving agent before it's ever delivered.

// Define agent contract
vektor.eval.schema({
  agent: "summarizer-v2",
  input: z.object({
    document: z.string().min(100),
    language: z.enum(["en", "de", "fr"]),
  }),
});

2. Loop detection

Circular delegation is rare but catastrophic — two agents call each other indefinitely, burning tokens and wall-clock time. We catch it by tracking call graphs in real time and firing an alert as soon as a cycle exceeds a configurable depth threshold (default: 3). In practice, the depth threshold matters less than the speed of detection: by the time a human notices a runaway loop in the dashboard, you've already paid for it.

3. Memory coherence

In long-horizon workflows, agents share semantic memory. Without coherence checks, one agent can quietly overwrite a fact another agent is about to read, and the rest of the chain proceeds confidently from a corrupted state. Vektor Memory v2 implements optimistic locking on every memory write and surfaces conflicts as span events in the trace view, so the collision is visible instead of inherited.

Takeaways

  • →Define input/output schemas for every agent in a workflow.
  • →Treat inter-agent messages as API calls — validate them.
  • →Track call graphs in real time to catch delegation loops early.
  • →Use optimistic locking on shared memory stores.
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