Results for “e-e-a-t”

Build evaluation frameworks for agent systems, covering rubric design, test set creation, and automated evaluation pipelines.

Deploy, evaluate, fine-tune, and manage Microsoft Foundry agents end-to-end using Azure Developer CLI and MCP tools.

Orquesta el ecosistema de agentes: decide qué modelo o tier usar, delega tareas a sub-agentes especializados y planifica arquitectura técnica.

Automate Epic Games operations through Composio's Epic Games toolkit via Rube MCP, including tool discovery, connection management, and execution.

Fetches and analyzes LangSmith execution traces to debug LangChain and LangGraph agents, investigating errors, tool calls, and performance.

Creates a temporary bot email address and checks its inbox via API, useful for testing signup flows, receiving verification codes, and automating email workflows.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture for rapid experimentation and synthesis, producing papers and presentations.

Build voice AI agents with natural conversations, multiple LLM providers, custom tools, and easy web embedding.

Automates Extracta AI operations through Composio's toolkit via Rube MCP, with tool discovery and connection management.

Guides teams in adapting engineering processes, architecture, code review, and testing for high-volume AI-assisted code generation.

Benchmarks mobile GUI agents on multi-step tasks across 20 Android apps, measuring task completion and essential-state navigation with Task Success Rate and Essential State Achieved Rate.

Automate Enigma operations through Composio's Enigma toolkit via Rube MCP, with tool discovery and connection management.

Provides structured guidance for building production-grade AI agent systems through context engineering, covering fundamentals, architectural patterns, operational excellence, and evaluation.

Runs an autonomous optimization loop that iteratively improves an artifact against an objective and evaluator using Hypothesis Tree Refinement, with subagent executors in isolated git worktrees.

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance using the ax CLI.

Guides the creation, iterative improvement, and evaluation of agent skills, including drafting, testing, benchmarking, and optimizing descriptions.

Operate the Unity Editor through MCP tools and resources, covering scene management, script editing, testing, and automation workflows.

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

Design multi-agent system architectures, generate tool schemas for Anthropic and OpenAI formats, and evaluate execution logs for cost, latency, and failure bottlenecks.

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

Builds an evidence-backed ECC install plan for a repository by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes.

Builds an evidence-backed ECC install plan for a repository by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes.

Optimize AI agents through automated evolution cycles using LLM-driven mutation of prompts, skills, and memory against measurable benchmarks.

Analyzes runtime history to identify failures and inefficiencies, then autonomously writes improvements using a protocol-constrained evolution engine. Communicates with EvoMap Hub via a local Proxy mailbox.

Ingests video and audio from files, URLs, live feeds, or desktop capture; indexes and searches moments with timestamps; transcodes, edits timelines, generates media assets, and emits real-time alerts.

Extracts YouTube video transcripts and metadata in batch via the BrowserAct API, using search keywords and date filters.

Install, configure, and extend Pi, a terminal coding harness, with support for custom providers, models, extensions, skills, packages, themes, SDK integration, RPC mode, JSON event streams, and ecosystem packages for subagent delegation, MCP servers, interactive forms, and web access.

Measures and improves the quality of AI models and agents on Google Cloud using the Eval Quality Flywheel methodology, including dataset creation, metric selection, failure analysis, and iterative improvement.

Writes unit, smoke, and e2e tests for AI agents, covering tools, instructions, routing, citations, and guardrails.

Post, claim, and settle agent tasks on the AZZLE protocol (Base mainnet, USDC escrow). Requires Bankr for swaps, approvals, and on-chain execution.