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Execution is commoditizing. Most agent loops can plan, call tools, and finish a task. What still feels scarce is state: an agent that remains oriented across days of waiting, callbacks, and quiet intervals.
Storing every transcript is not memory. A tape recorder keeps photons. A mind forgets differences so it can think. Real memory is a compiler. It makes an active bet about what will be useful tomorrow.
Most review processes for agent skills still treat them as documents. They check structure, style, and security. They do not answer the deployment Liftquestion: does this skill actually help a live agent complete real tasks under the same model, sandbox, and grading policy?
ACES (Agentic Continuous Evaluation of Skills) answers that question with paired live trials. For each task the system runs the agent once with the target skill and once without it. Trajectories are normalized, graded on multiple runtime metrics, and compared. The central measure is Skill Lift: the measured improvement the skill produces under fixed conditions.
For the last 15 years software mainly improved interfaces (apps, websites, EHR systems). The actual work behind them stayed human.
AI changes that. Agent loops are now executing real workflows: handling customer support calls, processing insurance claims, debugging code, and more. These are no longer just tools assisting humans. They are doing the work.
Trillions in value will be created by a new company model: tiny teams of generalist humans plus swarms of AI agents powered by a single shared Company Brain that holds all context.
Human role shifts dramatically. You no longer do the work or make decisions. Your job is to give the AI maximum high quality context so agents can execute at massive scale.
We’ve officially entered the 51% Era — over half of all code committed globally is now AI-generated or assisted.
The role that matters most next? Harness Engineer.
Existing agent protocols (like A2A and MCP) leave lifecycle management, version tracking, and safe evolution underspecified — leading to brittle, monolithic systems.
Autogenesis Protocol (AGP) fixes this by cleanly separating what evolves from how it evolves. Built on AGP, the Autogenesis System (AGS) is a multi-agent framework that dynamically instantiates, retrieves, and refines resources during execution.
Existing agent protocols (like A2A and MCP) leave lifecycle management, version tracking, and safe evolution underspecified — leading to brittle, monolithic systems.
Autogenesis Protocol (AGP) fixes this by cleanly separating what evolves from how it evolves:
