How are you evaluating AI features in your ERP/CRM suites?
Vendors are bundling copilots into every suite. How are you deciding what to switch on, and how are you measuring value?
In short
AI agents are software that uses a language model to plan steps and call tools, such as searching systems, updating records or running code, to finish a task with limited supervision. Buyers care because an agent acts with real permissions: what it can reach, how its actions are logged and who approves the risky ones matter as much as how capable it is.
Written by Techarda editors · Updated · How we work
The newest AI agents stories from the last 90 days. Each one opens with our summary and a link to the original source.
Learn how to move an agent session from the GitHub Copilot app into VS Code without losing the conversation, code changes, or working context.
Ask a platform team how many AI agents are running in their clusters and you usually get a pause. Agents arrive through application teams, not platform teams. They call tools over MCP, invoke other agents...
Why the controls on an AI agent have to live outside of it, and what a security operations team can implement and maintain those controls. The new privileged user in your environment Security teams spent two decades learning how to work with the people they trust most. Domain admins, DBAs, finance approvers and contractors with VPN access all get a mature playbook: verified identity, scoped…
Enterprise assistants still respond mostly in text. However, that can break down for visual, multi-step work, where every adjustment costs another slow chat turn. MCP Apps ( SEP-1865 ) solves this. This open Model Context Protocol (MCP) extension pairs a tool with a live UI. In Gemini Enterprise , that UI renders directly in the chat flow inside a sandboxed iframe, and can expand to fullscreen or…
Snowflake's data team built an AI agent that diagnoses monitored pipeline failure and opens a proposed fix in under 16 minutes. Here's how.
At a creative industry event that took place today in Berlin, hosted in collaboration with Adobe, Bertelsmann, NVIDIA and UFA, the latest advancements in intelligent media were on display. Key to the news: the European Broadcasting Union (EBU) is collaborating with NVIDIA to expand its sovereign framework with cloud, open models and integration […]
Discover how to secure AI agents in your cloud environment with Tenable One Cloud Exposure. We explore Microsoft Entra Agent ID and provide essential best practices for robust agentic AI identity security. Key takeaways Understand your current maturity level and then build an appropriate action plan to secure your agent identities. Use your cloud providers’ native tools or Tenable One Cloud…
Platform Beaver brings Port's agentic SDLC to Slack, with AI agents handling bug triage, incident response, PR review and Q&A.
Showing the newest 60 of 525. The feed archive lists every story by month.
Stories say what companies announced. These threads are where people who work with AI agents compare notes.
Vendors are bundling copilots into every suite. How are you deciding what to switch on, and how are you measuring value?
We are hearing a lot about agent pilots and far less about agents still running six months later. What was the first use case, what guardrails did you need, and is it still in production?
LangGraph/CrewAI-style frameworks versus packaged agent platforms from the big vendors. If you have tried both, where did each win on time-to-value, control and cost?
The active companies with the most AI agents stories in the last 90 days. It counts news coverage, not quality, market share or fit for your needs.
Where AI agents sits among the categories Techarda covers. Each hub shows who is active, and each guide sets out how to evaluate vendors.
Plain definitions of terms that come up in AI agents stories, from the Techarda glossary.
A chatbot answers questions. A copilot suggests actions inside an application while a person stays in control. An agent carries out a sequence of actions itself, calling tools and systems, and stops when it finishes or needs approval.
Treat each agent like a service account: its own identity, the narrowest permissions that do the job, short-lived credentials and a full audit trail. Avoid letting an agent act with a person’s full access.
The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data through a common interface, so one connector can serve many assistants and agents. Each connector is also a route into your systems and needs access controls like any API.
Common failure points are vague instructions, tools that return unexpected data, loops that repeat the same step, and prompt injection, where text in a document or web page tries to redirect the agent. Testing with your own messy data shows these quickly.
Stories are filed under topics automatically from their headline and summary, so the odd one may sit in the wrong place. Spotted one, or a topic we should add? Email engage@techarda.com. All topics.