This content originally appeared on DEV Community and was authored by PSBigBig
👉 Full index:
Global Fix Map README
🚨 The recurring nightmare
If you’ve ever tried to wire up multiple agents with AutoGen, crew.ai, LangChain, or your own orchestration layer, you’ve probably seen this:
- Two agents waiting for each other → process hangs.
- Memory wiped because last writer wins.
- Log file grows without bound while agents call each other forever.
- Planner and executor fight over who is responsible.
- Phantom subtasks reappear like ghosts and never terminate.
This isn’t your GPU’s fault or OpenAI’s API bug. This is coordination collapse.
🩸 What’s actually breaking
Multi-agent systems often fail because the orchestration layer has no contracts:
- Shared memory without isolation → agents overwrite each other.
- Task graphs with cycles → no cycle breaker, so deadlock is inevitable.
- Planner emits too many subtasks while executors choke → cascade.
- Role confusion → agents duplicate work or skip responsibility.
- Cleanup missing → phantom subtasks remain alive across runs.
The visible symptom is an infinite loop or “nothing happens,” but the true root cause is missing orchestration invariants.
🛠 Minimal fix patterns
- Scoped memory: isolate agent logs by ID; append-only history.
- Deadlock guards: detect cycles in the task graph, auto-terminate after N iterations.
- Role contracts: planner only emits, executor only resolves. No overlap.
- Heartbeat timeout: kill subtasks that fail to report progress.
- Traceability schema: every action carries task_id, parent_id, expiry.
✅ Acceptance targets
- Deadlock detection fires in ≤ 3 iterations.
- Memory overwrite incidents = 0 across parallel runs.
- Infinite loop cutoff ≤ 10s from spin detection.
- Phantom task survival = 0 after cleanup.
- Task trace reproducible 100% on rerun.
🧭 How to apply in practice
- Open the Global Fix Map README.
- Jump to the Multi-Agent Orchestration section.
- Apply the lock/role/traceability rules.
- Validate with the acceptance targets above.
📌 Why this matters
Without these guardrails, your multi-agent stack is a lottery. Sometimes it “just works,” but under stress (real user queries, long-running sessions) the system spins, stalls, or wipes memory. With contracts in place, orchestration becomes reproducible and debuggable — not haunted.
Next Episode (7): RAG Observability — how to stop your pipeline from lying about recall, and how to instrument ΔS + λ traces in production.
This content originally appeared on DEV Community and was authored by PSBigBig
PSBigBig | Sciencx (2025-09-12T04:59:34+00:00) # EP 6 — Why Multi-Agent Orchestration Collapses (Deadlocks, Infinite Loops, and Memory Overwrites in AI Pipelines). Retrieved from https://www.scien.cx/2025/09/12/ep-6-why-multi-agent-orchestration-collapses-deadlocks-infinite-loops-and-memory-overwrites-in-ai-pipelines-2/
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