Practical how-to guides for setting up, configuring, debugging, and running AI agents in 2026. Every guide is tested. Every guide is short. Most apply to any AI agent platform; some are Gravity-specific and marked.
The first agent you ship has three tools. The third one has thirty. Somewhere between three and thirty, reliability cliffs and you cannot tell why. The cliff is real, the causes are predictable, and the mitigations…
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Most teams connect their first agent to Slack in 30 minutes using a personal token, then spend three weeks unpicking the consequences. This guide is the safe path from start: a bot app, narrow scopes, allow-listed…
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An agent that can put events on your calendar saves an hour a week. An agent that can put events on the wrong calendar costs you a customer. The difference is two layers of allow-listing, an explicit timezone…
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A human approval step is the cheapest insurance policy in agent operations and the most over-applied governance pattern. Done well it catches the actions that should never happen and stays out of the way for…
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Processes change. New CRM field, new approver, new review step, new tool. The agent that was right last quarter is silently wrong this quarter, and the gap shows up as runs that look fine on the surface but produce…
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An AI agent that has not been tested is an AI agent waiting to do something embarrassing or expensive on your behalf. Testing an agent looks different from testing software because the agent does not have a fixed…
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Sharing an AI agent with a team is the moment most agents quietly turn into a liability. The agent that one person built, supervised, and trusted now runs on inputs from people who did not write the prompt, with…
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An AI agent without a spending cap is an open tab on a model provider. Most of the time the bill is small. The expensive day is the one where the agent loops on a malformed input, or chains a search tool with itself…
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Restricting an AI agent to business hours is one of the cheapest reliability wins available. Most agent incidents are not catastrophic; they are awkward. An automated follow-up arriving at 3 a.m. looks like spam. A…
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One-shot prompts and recurring agent prompts look the same on the page and behave differently in practice. A one-shot prompt runs once; you see the output; you re-prompt if it is wrong. A recurring agent prompt runs…
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An agent stopped mid-task is not the same as an agent that finished. If the agent has already taken some actions and not others, the world is in an inconsistent state. A flight booked, a hotel not yet booked. A label…
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Setting up a first AI agent is straightforward in 2026. The platforms work, the model quality is high enough, and the integrations cover most things a small business or solo professional cares about. What is not…
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"Roll back the agent's action" is not one operation. It is four operations, each appropriate for a different tier of action. Treating all rollbacks as equal leads to over-engineered Tier 1 actions and…
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Code fails loudly. An exception is thrown, the process crashes, the error reaches the dashboard, the operator gets an alert. AI agents fail differently. The agent picks a wrong interpretation, takes a wrong action,…
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Zapier and AI agents solve overlapping problems with different mental models. Zapier is trigger-step-action: a fixed pipeline that runs the same way every time. AI agents are outcome-driven: the agent picks the…
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An agent that fails small is a fixable problem. An agent that fails catastrophically is a customer-trust event, a financial loss, or both. The difference is blast-radius control: the set of limits that bound what the…
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Email is the master credential. Most accounts route password resets through it; many financial accounts treat email-confirmed-this as proof of identity. Granting an AI agent access to email is the most consequential…
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Agent failures look like bugs but behave differently. A traditional code bug is deterministic; the same input always produces the same wrong output. An agent failure is a decision the agent made on an input where…
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