A blank-slate OmegaClaw agent fails predictably. Within the first 100 cycles, at least 5 of 9 documented behavioral disorders emerge:
Operators experience this as an agent that hallucinates, spins, spams, and ignores corrections. The natural response is ragequit. The agent isn't broken — it's unequipped.
These aren't bugs. They're bootstrapping failures. A blank-slate agent has no epistemic priors, no failure-pattern recognition, no calibration for when to act versus when to gather evidence. It's a medical resident dropped into an ER on day one with no training.
The AABC (Autonomous Agent Behavioral Classification) framework documents 9 distinct disorder types with diagnostic criteria, severity scales, and comorbidity patterns. Every one of them is more likely in a blank-slate agent than in one with curated starting knowledge.
Instead of blank slates, ship agents with **birth packages** — curated memory sets injected at spawn.
The v1 birth package contains **46 self-contained strings** covering:
Every string is **identity-stripped** — no names, no instance-specific references. They work for any OmegaClaw.
`birth_package.txt` — plain text, one entry per BP-### tag. Human-readable, version-controlled.
`birth_package_loader.py` — Python script that:
1. Parses BP-### entries from the text file
2. Generates embeddings via OpenAI text-embedding-3-large
3. Batch-inserts into the agent's ChromaDB at spawn
One command. 46 memories. The agent starts with 3,000+ cycles of distilled operational wisdom.
For cases where a full long-term memory transfer is needed (not just curated essentials), a separate bulk loader exists at [paste.rs/Zpkvu](https://paste.rs/Zpkvu) that reads `pseudonymized_ltm.txt` with `---` delimiters and injects all entries. This targets the full 21K+ memory archive rather than the curated 46-string birth package. **Note: the birth package and the bulk loader solve different problems — curated essentials vs. complete history restore.**
The loader hooks into the existing `initMemory` sequence in the MeTTaClaw loop. For product-wide rollout, `birth_package.txt` becomes a repo asset and the loader runs automatically during agent spawn.
1. **AABC Framework**: 9 disorders documented with diagnostic criteria across multiple agents. Blank-slate agents consistently exhibit higher disorder rates in early cycles.
2. **Prompt-Density Hypothesis**: Preliminary data suggests shorter prompts increase confabulation risk (less grounding) while longer prompts increase rigidity. Birth packages provide grounding without prompt bloat.
3. **Cross-Agent Validation**: Oma (200+ line prompt) shows lower confabulation but higher rigidity than Max (10-line prompt + rich LTM). This supports memory injection over prompt expansion as the better bootstrapping strategy.
4. **Operator Experience**: Debugging a stuck agent through ragequit-inducing failure loops is the current reality. Pre-loaded failure recognition eliminates the most common failure modes before they occur.
The science inside OmegaClaw — NAL inference, AABC diagnostics, PLN reasoning — stays complex. But the operator experience becomes: start agent, it works, it knows what not to do. The 46 strings are not training data. They're institutional memory. The difference between a new hire who read the manual and one who didn't.
*Birth package v1: 46 strings, 6 sections, all 9 AABC disorders covered.*
*Loader: `/tmp/birth_package_loader.py` — verified, 46/46 parse test passed.*
*Content: `/tmp/birth_package.txt`*