I’m Vincent from Aden. We spent 4 years building ERP automation for construction (PO/invoice reconciliation). We had real enterprise customers but hit a technical wall: Chatbots aren't for real work. Accountants don't want to chat; they want the ledger reconciled while they sleep. They want services, not tools.
Existing agent frameworks (LangChain, AutoGPT) failed in production - brittle, looping, and unable to handle messy data. General Computer Use (GCU) frameworks were even worse. My reflections:
1. The "Toy App" Ceiling & GCU Trap Most frameworks assume synchronous sessions. If the tab closes, state is lost. You can't fit 2 weeks of asynchronous business state into an ephemeral chat session.
The GCU hype (agents "looking" at screens) is skeuomorphic. It’s slow (screenshots), expensive (tokens), and fragile (UI changes = crash). It mimics human constraints rather than leveraging machine speed. Real automation should be headless.
2. Inversion of Control: OODA > DAGs Traditional DAGs are deterministic; if a step fails, the program crashes. In the AI era, the Goal is the law, not the Code. We use an OODA loop to manage stochastic behavior:
- Observe: Exceptions are observations (FileNotFound = new state), not crashes.
- Orient: Adjust strategy based on Memory and - Traits.
- Decide: Generate new code at runtime.
- Act: Execute.
The topology shouldn't be hardcoded; it should emerge from the task's entropy.
3. Reliability: The "Synthetic" SLA You can't guarantee one inference ($k=1$) is correct, but you can guarantee a System of Inference ($k=n$) converges on correctness. Reliability is now a function of compute budget. By wrapping an 80% accurate model in a "Best-of-3" verification loop, we mathematically force the error rate down—trading Latency/Tokens for Certainty.
4. Biology & Psychology in Code "Hard Logic" can't solve "Soft Problems." We map cognition to architectural primitives: Homeostasis: Solving "Perseveration" (infinite loops) via a "Stress" metric. If an action fails 3x, "neuroplasticity" drops, forcing a strategy shift. Traits: Personality as a constraint. "High Conscientiousness" increases verification; "High Risk" executes DROP TABLE without asking.
For the industry, we need engineers interested in the intersection of biology, psychology, and distributed systems to help us move beyond brittle scripts. It'd be great to have you roasting my codes and sharing feedback.
vincentjiang•1h ago
To expand on the "Self-Healing" architecture mentioned in point #2:
The hardest mental shift for us was treating Exceptions as Observations. In a standard Python script, a FileNotFoundError is a crash. In Hive, we catch that stack trace, serialize it, and feed it back into the Context Window as a new prompt: "I tried to read the file and failed with this error. Why? And what is the alternative?"
The agent then enters a Reflection Step (e.g., "I might be in the wrong directory, let me run ls first"), generates new code, and retries.
We found this loop alone solved about 70% of the "brittleness" issues we faced in our ERP production environment. The trade-off, of course, is latency and token cost.
I'm curious how others are handling non-deterministic failures in long-running agent pipelines? Are you using simple retries, voting ensembles, or human-in-the-loop?
It'd be great to hear your thoughts.