Ai Agents
5 posts on Ai Agents.
AI agent governance for small teams: approvals, audit trails and observability
AI agent governance without a compliance department: decide what an agent may do alone, make the rest wait for approval, keep a record of every run and check the results. A practical setup for a small team, the risks it guards against, and what to log.
Read the postAI agents vs workflows: why a proactive agent still needs a workflow
An AI workflow follows predefined steps; an AI agent decides its own. When to use each, why recurring work needs both, and how a workflow written in plain words gives an agent's judgment a repeatable shape with a trigger, an output and a checklist.
Read the postProactive AI agents: what they do well, and the five things they still need
Proactive AI agents act before you ask: they watch your inbox, calendar and tools and take the first step. What they do well, and the five things work adds: approvals, a record of every run, corrections that stick, repeatable workflows and one place to run them all.
Read the postSelf-improving AI agents: continuous learning for automations that adapt to your feedback
A self-improving AI agent gets better from feedback: it runs, gets checked, takes your correction and remembers it next time. How the feedback loop works, what the research says, where self-improvement goes wrong, and how to keep an automation adapting while it stays under your control.
Read the postToo many AI agents? Why one orchestrator beats a dozen agent chats
AI agents multiply faster than anyone can manage them: one per app, one chat per job. Why agent sprawl happens, what it costs a small team, and how one lead agent with one inbox and one calendar replaces a dozen separate agent chats.
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