A controlled method for making GPT-5.6 prompts leaner without deleting the constraints that keep production agents reliable.
Drone-Bench and AI-controlled F-16 tests show rapid capability progress. They also reveal the evidence required before autonomous flight can be truste
GEPA optimize_anything makes the evaluator the stable interface for prompts, code, agents, and configs. Here is how to design one safely.
A practical workflow for AI agents in mathematical proofs: durable state, hostile audits, blind reconstruction, and evidence-gated knowledge.
AI agent sandbox persistence varies by lifecycle action. Use this five-layer model to verify files, memory, volumes, connections, and side effects.
"Learn when an AI agent needs a domain-specific language, when JSON Schema is enough, and how to build a minimal DSL with deterministic validation."
MCP Elicitation turns missing input into recoverable control flow. Learn form vs URL mode, state handling, security boundaries, and retries.
When OpenAI partnered with PwC to create a CFO alliance, they bet on a deeper trend: the bottleneck in corporate finance has shifted from calculation
"Anthropic ran a week-long experiment where Claude autonomously traded items across 4 parallel markets. Opus agents sold items for 70% more than Haiku
"GPT-5.5 is OpenAI's first fully retrained foundation model since GPT-4.5. It delivers 88.7% on SWE-bench Verified, 82.7% on Terminal-Bench 2.0, and m