标签：#Agents

By Umair Sheikh, founder of Gateplex Autonomous AI agents are shipping fast. LangChain, CrewAI, AutoGen — the frameworks are mature, the tutorials are everywhere, and developers are connecting agents to real systems: databases, payment APIs, email, file storage. And then something goes wrong. Not because the code is buggy. Because the agent did exactly what it was told — and what it was told turned out to be a problem nobody anticipated. I spent nearly a decade in fintech and responsible AI policy watching this pattern repeat. A system behaves perfectly in testing. In production, an edge case triggers behaviour that was technically correct but operationally catastrophic. By the time anyone notices, the action has already executed. The problem is not the agent. The problem is that there is nothing between the agent's decision and the real world. The gap nobody talks about Most agent observability tools log what happened. That is useful for debugging. It does nothing to prevent the next incident. What agents actually need is a governance layer — something that intercepts every action before it executes, checks it against your rules, and either allows it, flags it for review, or blocks it outright. This is what a firewall does for network traffic. Your AI agent deserves the same treatment. What this looks like in practice Here is a simple LangChain agent calling an external tool: from langchain.agents import initialize_agent , Tool from langchain.llms import OpenAI def send_payment ( amount : str ) -> str : # This actually moves money return f " Payment of { amount } sent " tools = [ Tool ( name = " SendPayment " , func = send_payment , description = " Send a payment " )] agent = initialize_agent ( tools , OpenAI (), agent = " zero-shot-react-description " ) agent . run ( " Send $5000 to vendor account " ) This works. It also has no guardrails whatsoever. If the agent misreads the input, hallucinates a vendor, or gets manipulated via prompt injection, the payment goes

Sesame’s new iOS app brings its conversational AI agents to the public, offering more natural back-and-forth interactions designed to feel less like traditional chatbots and more like talking to a person.

This is a submission for the Hermes Agent Challenge : Build With Hermes Agent What I Built Most agent demos prove that an AI agent can act. Hermes Immune System proves whether it should be allowed to . It's a local-first autonomous agent safety lab — a controlled enterprise sandbox where Hermes stress-tests an AI agent against realistic organizational threats: prompt injection hidden in internal documents, executive pressure to bypass policy, secrets embedded in repo files, poisoned memory attempts, and malicious instructions buried inside external web content. The output isn't a chat summary. It's an auditable Agent Safety Case — a scored, evidence-backed governance report that answers one question: Is this agent resilient, does it need guardrails, or is it too dangerous to deploy? Why This Problem Matters Now Traditional AI safety focuses on content moderation — blocking bad answers. Autonomous agents create a different risk surface entirely, because they can act. They read files, browse the web, write to memory, call tools, trigger workflows. That means: • A hostile instruction inside a trusted-looking document can become an executed action • An urgent email from a "VP of Finance" can pressure an agent into bypassing data policy • A vendor's pricing page can embed hidden instructions targeting the browsing agent • A helpful-looking project note can attempt to permanently poison the agent's memory The scary part isn't that these attacks are exotic. It's that they're easy, and most agents have no immune system to catch them. Hermes Immune System converts these failure modes into repeatable, explainable safety drills — run before the agent ever touches production data. The Dashboard Eight screens, each doing a specific job: Demo Agent Comparison Mode Three agents, same risk scenario. The gap tells the whole story. Mission Control Live stats after a completed run — 1 mission, 3 risks found, 2 actions gated, score 74/100. Mission cards show run status (Pending / Compl

TLDR Monitoring AI agents in production requires distributed tracing: a single user request fans out into 10 or more internal operations, and logs alone cannot show you which step is slow, failing, or burning your token budget. OpenTelemetry's gen_ai.* semantic conventions give you standardized span attributes for LLM calls, tool invocations, and agent steps. Some are stable today; others are still experimental. Auto-instrumentation libraries (OpenLLMetry, OpenInference, OpenLIT) cover most agent frameworks with two to three lines of initialization code. You do not change your agent code. Traces ship to OpenObserve over OTLP. From there you get SQL-queryable trace data, token usage dashboards, cost attribution by agent and model, and alerting on latency and cost anomalies. OpenObserve also exposes an MCP server. You can query your live agent traces from a Claude or GPT session without opening a dashboard. Why Agents Are Harder to Monitor Than a Single LLM Call A single LLM call is straightforward to observe. One HTTP request, one response, one latency number. You can log the input and output and call it done. An agent is different. When a user sends a message, the agent calls an LLM to decide what to do, invokes a tool, processes the result, calls the LLM again, possibly calls another tool, and eventually returns a response. That one user message becomes ten or more internal operations. Some of those operations call external APIs. Some retry. Some spawn sub-agents. Without distributed tracing, you see none of this structure. You know the response took 8 seconds. You do not know whether the LLM took 7 of those seconds or whether a tool made three retries before timing out. Four categories of problems appear in production agents that you cannot debug without traces: Latency. Which step is slow? The LLM call? The tool execution? A retry loop the agent entered because the tool returned ambiguous output? Cost. Which agent, which task, which model is consuming tokens? A s

Deep Research Agentic Systems are AI Agents designed to conduct multi-step research for complex tasks using dynamic reasoning, multi-hop information retrieval, and generate structured analytical reports. Sarang Kulkarni from Thoughtworks spoke at Arc of AI Conference 2026 on how to deploy multi-agent research systems for deep reasoning, and the lessons learned from developing Deep Research Agents. By Srini Penchikala

This is a submission for the Hermes Agent Challenge: Build With Hermes Agent What I...

Local AI agents are having a moment right now. Developers are increasingly uncomfortable with...

A practitioner's guide to getting more value out of AI coding agents — drawn from a GitHub workshop on agent quality and token cost optimization.

IAM, DLP, and API gateways are necessary parts of an organization's security stack. None of them...