103 stories
TL;DR Agent-to-agent communication is becoming an important research area for AI companies. As agents grow more capable, their ability to communicate, plan, challenge one another, and work toward a shared objective may produce gains that cannot be achieved by improving each agent independently.
Looking for a Hermes Agent alternative? Six options compared on setup time, hosting, and cost, including when staying on Hermes is the better call.
Compare six open-source AI agent frameworks by language, workflow control, tools, and use case, including Mastra, LangGraph, CrewAI, and Pydantic AI.
For the August VB Pulse, VentureBeat Intelligence surveyed respondents from organizations with 100 or more employees. We asked which agent orchestration platforms they run and why, what they plan to adopt next, where they expect agent control to sit and how they stop runaway agent spending.Read more
Manual code tweaking isn't a testing strategy. Here is how to actually evaluate AI agents for production. For more information, check out the Agent Clinic episode linked below the title.
Compare 9 AI agent observability tools on what each shows for a multi-step run: handoffs, sessions, MCP calls, agent evaluators, framework support and hosting.
Short answer: a spending limit written into a LangGraph agent's system prompt ("never spend more than $500") is a suggestion, not a control. Any text the agent reads, including a scraped invoice or an email, can contain a competing instruction, and the model has no way to know which one is…
Developers are skeptical by default, and that is a good thing when choosing a Gen-AI course. Here is a technical checklist to score any online GenAI course before you enrol. The scoring checklist Give each item 1 point. A strong course should score 10 or more out of 12.
A durable harness commits every step of an agent run to storage, so a crash resumes mid-turn. What Pi Durable shipped on 1 October, and what is only claimed.
A year ago I wrote about building a tiny AI agent that uses MCP tools. It’s still the most-read thing I’ve published.
I watched a research pipeline die at step nine of ten. Forty-three minutes of work, sixty sources fetched, four analyses complete, one synthesis in progress. The worker crashed—OOM, not a logic bug. When it came back, the orchestrator did what I'd told it to do: it started over.
OpenAI, Anthropic, and Google each use a different request and response shape for the same tool. Agent frameworks handle that difference in different places.
Multi-model orchestration is three layers. Workflow orchestration is planning, state, memory, and delegation, and LangGraph and CrewAI are built for it. Model routing is choosing a model per call and falling back when it fails, and provider routing is choosing which provider serves that model.
Ambient agents respond to events such as an Amazon S3 upload, a schedule, or an alert instead of waiting for a chat prompt. This post walks through building framework-agnostic ambient agents on Amazon Bedrock AgentCore using Amazon SQS, AWS Lambda, and Amazon DynamoDB, with a single ask_human tool…
Agent Droplets are available today for DigitalOcean Managed Agents in public preview. If you already run agents on Claude Code, Codex CLI, or OpenCode, point them at DigitalOcean and they can run on an Agent Droplet with minimal setup.
AI agent monitoring explained: tool calls, side effects, handoffs, cost and loops to watch, how to alert on drift, which tools help, and what monitoring misses.
One run, and what it actually proves The post Towards Spec-Driven Test Automation: Part 2 appeared first on Towards Data Science.
Connect a LangGraph agent to Apify through the official langchain-apify integration or Apify MCP Server, and give it real-time web search, scraping, and social media data.
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