Results for “agent-continuity”

Orchestrates multi-agent project builds with persistent state, parallel execution, and atomic git commits.

Debug agent execution by querying trace and metric tables, analyzing tool calls, durations, errors, and performance trends.

Design and build hosted agent infrastructure with sandboxed execution, warm pools, session persistence, and multiplayer collaboration.

Drafts structured goal-loop prompts for long-running agent work with verifiable stop conditions, validation commands, and documentation requirements.

Provides patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.

Implements a continual learning loop for AI coding agents using hooks, two-tier memory (global and local), and automatic pattern detection to persist and apply learnings across sessions.

Automates the full lifecycle of agent skills: extracts reusable constraints from user feedback, merges preferences with version bumps, and retrieves relevant skills for new tasks.

Designs persistent memory architectures for AI agents, covering cross-session knowledge retention, entity tracking, temporal validity, graph/vector retrieval, and memory consolidation.

Build persistent multi-agent operating systems on Claude Code with kernel architecture, specialist agents, slash commands, file-based memory, and scheduled automation.

Operate long-lived agent workloads with observability, security boundaries, and lifecycle management.

Compare coding agents head-to-head on reproducible tasks with pass rate, cost, time, and consistency metrics.

Provides persistent, compounding memory for AI agents across sessions using local markdown files, with optional Supabase-backed semantic search.