Feb-25-2026 AgenC Devlog: Screenshot: AgenC Grok agent in a sandboxed AgenC OS. Writing code, debugging, installing what it needs and then playing the video game it created. Zero prompting beyond the initial task. Prompt: "Create a full snake game in Python using tkinter. The game should have a dark theme with glowing neon green snake, score tracking, and game-over screen with restart. Launch it on the desktop so I can watch you build and test it." Shipped 4 PRs that turn a flat ReAct loop into a layered agentic system with persistent memory, self-learning, and resumable workflows. → Semantic memory: vector-backed hybrid search, cosine 0.7 + BM25 0.3 with recency re-ranking, 24h half-life, greedy-packed into a 2000-token budget. Auto-selects Ollama/Cloud/Noop. Zero config. → Planning + compaction: system prompt planning instruction, low risk, zero executor changes, 80% of the benefit. Budget compaction instead of hard failure: summarize older messages, keep system + summary + last 5, fire hook to store summary, retry. → Self-learning + auto-screenshot: ChatExecutor reads learned patterns from KV, confidence >= 0.7 and injects them per-message. Desktop actions auto-capture screenshots after 300ms, merged inline into tool results so vision LLMs see outcomes without extra turns. Optional response evaluator/critic. → Progress + pipelines: persistent progress entries via tool:after hooks survive daemon restarts. PipelineExecutor with checkpoint/resume, approval gates, and per-step error policies, abort/skip/retry. Fully serializable checkpoints. → Desktop viewer: embedded noVNC panel right in chat. Auto-opens when a sandbox goes ready. Split layout with a monitor toggle in the header. View-only with autoconnect, scale resize, and clipboard permissions. Unifying pattern: MemoryRetriever interface, shared KV backend, hook-driven side effects. Every layer toggleable independently.