Results for “agent-to-agent”

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

Design tool descriptions, schemas, and response formats that agents can reliably route on, including MCP server design, tool-set consolidation, and naming conventions.

Orchestrates building a new feature end-to-end by delegating research, planning, TDD implementation, review, and gated commit to specialized agents.

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

Access 31 pay-per-use tools for quantum security, agent safety, research, data, and earn on Base, paid via x402 protocol.

Analyzes Android apps to identify user workflows for AppFunctions, generates Kotlin code to expose them to the Android system, and refines KDoc documentation for AI agent compatibility.

Optimizes agent context setup by structuring rules, specs, source files, error output, and conversation history to improve output quality.

Build LLM-powered applications using the Claude API, Anthropic SDKs, or the Agent SDK with language-specific guidance and best practices.

Provides 25 paid HTTP endpoints on Base mainnet that bill agents per call in USDC for web search, AI analysis, crypto and stock data, SEC filings, company intelligence, news, sentiment scoring, and a macro dashboard, returning structured JSON.

Builds reliable browser automation skills through iterative experimentation, running an inner agent to browse sites and improving navigation instructions until tasks pass consistently.

Inspect and adjust the agent's runtime state, including model, context window, iteration limits, token usage, workspace configuration, subagent status, and request routing metadata.

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

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

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

Configure an AI agent's personality using the AURA protocol based on the HEXACO model, adjusting traits like honesty, assertiveness, and autonomy to reduce sycophancy and tailor communication style.

Adds input and output guardrails to an agent, including validation, content filtering, domain scoping, and rate limiting, with TypeScript examples and wiring instructions.

Shares memory between agents at the representation level by compacting the orchestrator's KV cache for efficient worker handoff, reducing token costs without summarization or retrieval.

Analyzes prompt injection, retrieval poisoning, memory contamination, planner drift, and tool-boundary abuse in agentic systems, mapping trust boundaries and proving exploit chains.

Analyzes past agent sessions to identify recurring failures and skill gaps, then automatically creates or patches skills and runs self-evolution to fix them, tracking improvement over time.

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.

Designs and reviews feedback loops for AI agents to ensure goals are machine-decidable, loops are damped, and human judgment is preserved.

Build AI applications with the inference.sh Python SDK: run apps, build agents, and integrate with 250+ models using sync/async, streaming, file uploads, and a tool builder API.

Uses a multi-agent generator-evaluator feedback loop to build high-quality applications from a single prompt, inspired by GANs and Anthropic's harness design.

Verify supply chain integrity for AI agent plugins, tools, and dependencies by generating SHA-256 manifests, detecting tampered files, auditing dependency pinning, and enforcing promotion gates.

Orchestrates a multi-agent build session, acting as manager to cut goals into units, spawn implementer and tester subagents, and run test-and-review loops with anti-thrash guardrails.

Prevent repeated AI coding-agent mistakes by turning failures into durable instructions, drift checks, regression tests, failure memory, and adoption reports tailored to the target repository.

Build a local browser agent that enforces a domain allowlist via CDP Fetch interception, using a safe_browser tool that owns the Playwright session and blocks off-domain requests.

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

Adds a persistent pattern or rule that Ralph will remember and apply to future stories.

Guides usage of the Gemini API on Agent Platform with the Google Gen AI SDK, covering SDK usage (Python, JS/TS, Go, Java, C#), capabilities like multimodal inputs, tools, media generation, caching, batch prediction, and Live API.

Discover, find, compare, audit, repair, adapt, craft, run, debrief, and prepare repeatable AI-agent loops for publication. Use when a user asks to analyze code or coding threads for recurring work, find a published loop, interview them to turn a goal into a bounded loop, review a loop...

Add plugin tools, connect MCP servers, or chain existing tools in one script within the LaRuche agent environment.

Recommends the best skill, agent, or command for a given task by classifying intent and scanning available tools.

Integrate 250+ AI models into JavaScript/TypeScript apps with streaming, file uploads, and agent tooling.

Integrates Clock Address Naming with MCP agents to locally stamp, verify, and log tool outputs with hashes and timestamps.