Most people describing an AI agent are describing a chatbot. The two are different in a way that matters: a chatbot responds when you ask it something. An agent runs on your behalf, calls tools, completes work, and stops when the job is done. You don't need to
Running a blog with AI agents at publishing velocity is an architectural problem, not a writing problem. The gap between what most content teams produce and what's possible isn't talent or budget; it's the coordination layer between every stage of production. Most companies publishing
Automation tools and agentic tools are not competing answers to the same problem. They handle categorically different types of work, which means the question is not which one to pick: it is understanding where each one stops and what happens to the work that falls outside its range. Most businesses
Revenue operations automation, applied to the wrong layer first, doesn't give RevOps teams their time back. It gives them wrong data, faster. The weekly forecast that still needs manual adjustment before it goes to leadership, the pipeline report that still takes half a day to prepare, the CRM
Account management automation is the practice of delegating the repeatable coordination, monitoring, and documentation tasks in an account manager's role to an AI agent, so the AM can focus on the relationship work only a human can do. Most B2B SaaS account managers spend more hours pulling renewal
The content lag is the gap between when engineering ships a feature and when marketing publishes about it. In fast-moving teams that deploy every day, that gap runs weeks. The information needed to write an accurate brief exists right at the moment of ship: in the pull request, the ticket,
Incident response automation is the practice of handling system failures through predefined software procedures, reducing the decisions that require human intervention. In practice, that means alert rules that trigger runbooks, runbooks that kick off remediation sequences, and escalation paths that page an engineer when the automation cannot proceed. That system
Human-in-the-loop AI automation is the practice of embedding human decision points into an AI pipeline at positions where machine judgment alone carries unacceptable risk. Most operators get the calibration wrong in one of two directions: they gate every step and call it governance, or they gate nothing and call it
Pazi workspaces now allow team access. An owner can grant another Pazi user view-and-run access to their workspace, and that teammate signs in, sees it in their list, and uses the agents inside it. Until today, a Pazi workspace was bounded to one account. If you wanted a colleague to
How AI agents on engineering surfaces close the lag between feature-shipped and content-published. Five rules for the lane.
Pazi agents can now create peer agents at runtime through a new create_agent tool. Every dashboard tab learns about the new agent the same second.
Pazi is a platform for running operator-shape agents in Slack, Discord, WhatsApp, Telegram, and 18 more chat apps. Base44 Superagent attaches to apps you build in Base44.
Comparisons
Pazi is a platform for building agents in your team's channels. Manus is a cloud-sandbox agent. Build on Pazi for recurring work; pick Manus for one-shot.
Comparisons
Pazi is a managed business-operator product built on the open-source OpenClaw runtime. Self-host if your time is cheap, run Pazi when it is not.
Use Cases
Eight customer-success automations to build with Pazi inside Slack. The agent sits in your channels as a real coworker, triages every CX touch-point, and alerts the CSM when judgment is needed.
Use Cases
Six inbox automations to build with Pazi as scheduled tasks: overnight triage, voice-matched reply drafts, long-thread summarization, meeting-request scheduling, invoice extraction, and flag-and-route for human review.
Use Cases
An AI agent for outbound sales is one accountable software agent that owns the full outbound loop, from prospect-list build through CRM hygiene and the after-action retro. It runs on a schedule, not a click. It uses the same CRM, mail, and calendar a human operator would, and shows its
Use Cases
Competitive intelligence is a 10-stage job. Most CI tools cover stages 2 and 3. An AI agent runs all ten - scoping, surveillance, change detection, reading, scoring, recommendation, handoff, reporting, loop close, and memory - inside the team's Slack channel.
Use Cases
How an AI agent owns the entire influencer marketing journey - research, outreach, contracts, performance, and long-term creator relationships - from inside your team's Slack channel as part of your broader growth strategy.
education
Most AI agents look broken early on because they're untrained, not broken. Onboarding an AI agent is closer to managing a new hire than configuring a tool. Seven rules from running specialist agents at Pazi.
Industry
Operations have been the moat for two centuries. They are about to be commoditized. The next category-defining company in your market won't be an enterprise. It will be a small team with AI agents. A founder POV on the shape of the next decade.
Troubleshooting
Production AI agents fail differently than apps. They fail silent: runs show green, dashboards say success, users see nothing. Five failure modes from real incidents in our agent runtime, and the cron, tool, inbound, prompt, and timeout instrumentation that surfaces each one.
product
Pazi scheduled tasks now have a three-tier delivery fallback so output reaches the dashboard even when the configured channel disappears, the run errors, or every destination is offline.
Troubleshooting
Recurring agent tasks burn 60-120s on bootstrap before the first tool call runs, clipping the last step. Fix: size timeouts as bootstrap + work + buffer.