Vector Databases in the Agent Stack: Still Necessary, or Already Legacy?
Short answer: vector databases are no longer the default they were in 2023, but declaring them dead is premature. For agents working over la
The runtimes, protocols, and plumbing beneath agents. · 55 articles
Short answer: vector databases are no longer the default they were in 2023, but declaring them dead is premature. For agents working over la
Stateful agents need a deliberate plan for where their working memory lives, how it persists across long-running tasks, and how it recovers
Most agent workflows eventually hit a wall: the system they need to act on has no API, only a website. Browser automation is the layer that
Agentic AI sold as a service lives or dies on reliability, not raw model quality. The hard part isn't getting an agent to work once in a dem
Most agent platforms quietly burn money by sending every request to their most expensive model. A model-routing layer fixes that by deciding
The market for Agentic AI-as-a-Service is being built on a tower of half-baked, competing protocols, MCP for tools, A2A for agent-to-agent
AI agents only deliver on the per-task, per-outcome promise of Agentic AI-as-a-Service if they can talk to each other and to your tools with
The Model Context Protocol (MCP) is an open standard that lets AI agents talk to your tools, data, and APIs through one consistent interface
The honest answer is that the open-source-versus-proprietary question is the wrong frame for most teams shipping agentic AI-as-a-service. Wh
The bottleneck for agentic AI-as-a-service isn't whether the models are smart enough anymore. It's whether there are enough GPUs to run them
The orchestration framework is the part of an agentic AI system that decides what happens next: which tool fires, which model answers, when
SaaS apps were built for humans clicking screens, so their data layers optimized for transactions and dashboards, not for autonomous agents
An agent runtime is the layer that actually executes an agent's loop: it holds state across steps, calls tools, recovers from crashes, enfor
Agent-to-agent (A2A) protocols are the emerging standards that let independent AI agents discover, authenticate, delegate to, and coordinate
Every agent platform needs to tell a model what tools exist and how to call them. The problem: there's no single, agreed-upon way to write t
Vendor lock-in for AI agents is rarely about a single switching cost, it's a dozen small dependencies that compound until migrating feels i
Edge agents move autonomous AI workflows out of the central cloud and onto hardware that sits next to the data they act on -- a factory gate
The agent stack stopped being a research curiosity and became a procurement category this year. The pattern is clear: orchestration is conso
Human-in-the-loop (HITL) checkpoints let an autonomous agent stop, hand a decision to a person, and resume exactly where it left off. The ha
Vendors across the agentic AI market have started calling their products an "agent operating system." Most of them aren't. A real agent OS w
Multi-agent coordination is the discipline of getting several AI agents to share work, hand off context, and converge on a result without st
Agent simulation environments are controlled, repeatable sandboxes that let you run an AI agent against fake-but-realistic tools, APIs, and
Every token an agent reads costs money and adds latency, and a context window is finite, so the real engineering problem in Agentic AI-as-a
Most APIs were built for deterministic software, not for probabilistic agents that improvise. The adaptation layer is the connective tissue