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.
An agent that logs meetings and nudges stale deals gives a RevOps team hours back every week. An agent holding a full-scope token to your CRM is one bad prompt away from overwriting a quarter of lifecycle stages. The…
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Most teams connect their first agent to Notion in ten minutes, then discover a week later that it either sees the entire workspace or silently reads only the first hundred rows of a database. This guide is the safe…
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To build a research agent, you set up one repeatable pattern: gather information from defined sources, verify it, then synthesize a summary you can trust. In practice that means deciding the exact research question,…
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To share an AI agent with your team, give most people run access and reserve edit access for one or two owners; run the agent on a shared team plan so billing is centralized and attributed per run; and keep a shared…
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To set up a multi-agent handoff, split the job at the points where the work changes character, define a clear contract at each boundary stating what data and state pass and what "done" means, pass the finished…
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To set up AI agent notifications well, decide three things: which events deserve an alert, which channel each event goes to, and who needs to act on it. The events worth notifying on fall into four classes:…
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A fallback chain is an ordered list of backup steps an AI agent runs when its first approach fails. The pattern is primary, then secondary, then tertiary: try the preferred method, and if a defined failure condition…
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To rate limit an AI agent, put three caps in front of every action it takes: a request-per-minute cap so it stays inside the quotas of the models and APIs it calls, a concurrency cap so it never runs more tasks at…
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To give an AI agent access to multiple tools, you connect each tool or API one at a time, scope its permissions to the least privilege it needs, give the agent a clear description of what each tool does so it can…
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To debug an AI agent tool error, read the run trace to find the earliest step that failed, identify which class of error it is, reproduce that single tool call in isolation, then fix it at the layer where the fault…
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To create an AI agent from a template, pick a prebuilt agent that already does the job you want, then customize four things: the inputs it reads, the trigger that starts it, the tools and accounts it connects to, and…
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To connect an AI agent to a database safely, give it a dedicated, scoped credential, start with read-only access, expose the schema so it writes correct queries, validate and parameterize every query before it runs,…
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To build an AI agent with memory, you build two layers and connect them. The first is short-term working memory, the text the agent holds inside its context window during a single run. The second is long-term…
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Most people who ask how accurate their AI agent is are really asking the wrong question. They reach for a model benchmark number, a leaderboard score, a percentage from a vendor deck. None of that tells you whether…
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To set spending limits on an AI agent, give it three controls that work together: a per-run cap so one task cannot run away, a budget cap so total spend stays inside a window, and an auto-pause that stops the agent…
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To schedule an AI agent with cron, you write a five-field expression that says when the run should fire, attach it to the agent, and set the time zone explicitly. A cron schedule turns a one-off agent into a…
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An agent that never fails does not exist. APIs time out, tools return garbage, a model invents a step that was never asked for, and a permission check blocks an action halfway through a task. The question that…
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A team workspace for AI agents is a shared space where everyone buys, runs, and governs the same agents under one set of rules, instead of each person quietly building their own. The short answer to "how do I create…
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Connecting an AI agent to a private API is what turns a chatbot into something that does real work. The agent stops describing what could happen and starts reading your inventory, creating tickets, or pulling a…
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A data pipeline moves data from where it lives to where it is useful, cleaning and checking it on the way. The hard part is rarely the happy path. Sources change a column name overnight, a feed sends half its usual…
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To audit an AI agent's decision history, you reconstruct a single run from its log: the input it received, the plan it formed, every tool it called with the arguments and results, the final output, who triggered it,…
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A prompt is not documentation; it is the logic of the agent. Change a line and you change how the agent behaves, sometimes for the better, sometimes in ways you only notice three days later when results quietly…
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An agent that passes every test in the lab can still fail on its first real day. Test data is tidy; the real world is not. Going live is its own discipline, separate from proving the agent works on known cases, and…
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Some jobs are too big for one agent. Producing a competitor analysis means researching several companies, summarising each, and assembling a comparison; doing all of that in a single agent stretches it thin and makes…
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Most useful agent work is not a single action. Chasing an overdue invoice means pulling the ledger, finding what is overdue, drafting a message for each client, sending it, and logging the result. That is a…
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The fastest way to lose trust in an agent is to let it do something irreversible that it got wrong. The fix is not to supervise everything, which would defeat the point of automating, but to design the agent so a…
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Choosing an AI agent platform looks easy until you have to defend the decision to a CFO, a security review, and a procurement officer simultaneously. The marketing pages are interchangeable. The pricing is opaque…
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If you can build an AI agent in n8n, LangChain, make.com, or any of the other workflow tools that have eaten the last three years, you can earn from it. The path from a working workflow to actual recurring revenue is…
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"Training an agent on our docs" usually means fine-tuning in marketing copy and retrieval-augmented generation in code. The marketing version sounds fancier; the code version actually works for what most companies…
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Rate limits are the boring failure mode that takes out demos when they go to production. The agent runs fine on five test requests, then 500 requests arrive in a minute and a third silently disappear. This guide is…
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