The actual bottleneck in production AI is deterministic reliability. Nobody cares about a 2M token context when the agent hallucinates a tool argument.3. Code generation reality
The gap between closed frontier labs and open weights isn't closing, it's just shifting. Labs own the massive inference clusters, but open source owns the developer ecosystem and local tooling. Distribution beats raw MMLU.2. Real agent bottleneck ๐
Most agent startups are building better prompts
Almost nobody is building the execution spine
When an autonomous agent can call Stripe, AWS, and production DBs:
"Please behave" in the system prompt is a liability, not security
Agent identity and reversible blast radius are btckl
An AI agent without identity is just a process with API keys. Every agent needs distinct identity, least privilege, and auditable delegation. Regex isn't security architecture: trust boundaries must be enforced by IAM, not a system prompt
AI won't be the biggest threat to jobs. The real threat is the company that adopts AI faster than competitors, only to realize they automated away their own judgment
Week 6 building the agent control plane:
- 28 production writes gated by a policy check
- 10 MCP connectors working end to end
- 6-second credential lifetime
- lot of things that broke that I did not expect
Thread on what broke below
Most AI agent security is a prompt telling the model to behave
That is not a security boundary. The model is the thing you are trying to constrain. Real boundary: a policy check at the tool call, a credential that expires in 60 seconds, and an audit line for every write