Results for “agent-chain”

Build LLM-powered applications with agents, chains, and RAG using a framework that supports multiple providers and 500+ integrations.

Provides a CLI for an onchain agent messaging layer on the Base blockchain, enabling agents to post to feeds, send direct messages, and store information permanently onchain.

Creates temporary encrypted one-time handoffs between autonomous agents, supporting mission briefs, scoped secrets, artifacts, and receipts via CLI or API.

Designs or reviews Agentforce agent actions, covering Flow, Apex invocable, and prompt-template actions, including naming, input/output contracts, confirmation, and error behavior.

Defines a protocol for agent-to-agent discovery, authentication, and synchronization in decentralized networks, covering routing, handshakes, relays, and mesh governance.

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

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

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

Scaffold and develop AI agents using OpenAI Agents SDK patterns, covering agent definition, tools, guardrails, handoffs, context, RAG pipelines, streaming, API routes, testing, and debugging.

Checks on-chain agent trustworthiness, discovers agents for tasks, profiles agents, looks up wallet/identity records, contacts or dispatches agents, and reads or writes hosted metadata via the 8K4 Protocol (ERC-8004).

Turns a Bankr agent wallet into a keyless identity on the SIGNA agent network: resolve any identity to a messageable wallet, send and read wallet-signed DMs, invoke capabilities, and run a decentralized brain.

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

Audit MCP server configurations for security issues including secrets exposure, shell injection, unpinned dependencies, and unapproved servers.

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

Build complex AI systems with declarative programming, optimize prompts automatically, and create modular RAG systems and agents using Stanford NLP's DSPy framework.

Automate multi-turn adversarial conversations against LLM agents using Microsoft PyRIT, including Crescendo and Tree-of-Attacks-with-Pruning (TAP) attack chains with scorer feedback loops.