Building autonomous AI agents. Notes from the team building Gravity. AI workflows, the future of recurring work, and what we learn along the way.
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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An AI agent is a software system that perceives its environment, reasons about a goal, and takes actions using tools or APIs, typically with human oversight available at key steps. An autonomous system operates…
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Grounding is the practice of tying an AI agent's outputs to verifiable external sources: retrieved documents, live tool results, database records, or structured reference data. Hallucination is the opposite…
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An AI agent can automate the repetitive card management work that keeps a Trello board running: creating and routing new cards to the right list, moving cards when their status changes, sending due-date nudges to…
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Yes, an AI agent can automate Monday.com status updates end to end. It reads item and group statuses across your boards, builds a plain-language rollup, flags anything overdue or stuck, and posts the result on a…
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Yes, an AI agent can automate the most tedious parts of Jira backlog grooming: scanning for stale tickets, identifying duplicates, flagging issues that are missing estimates or acceptance criteria, and surfacing…
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Yes, an AI agent can automate Gmail label application: reading each incoming email, classifying it by sender, topic, and intent, and applying the right label or set of labels without you touching the message. The key…
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Yes, an AI agent can automate the structural work of running a Front shared inbox: reading every incoming conversation, classifying intent, assigning to the right person, tagging for reporting, drafting a suggested…
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An AI agent can enrich Airtable records automatically: it reads new or incomplete records, looks up missing field values from external sources, writes the enriched data back into the correct fields, flags potential…
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Emergent behavior in AI agents refers to actions, strategies, or outcomes that were not explicitly programmed but arise from the interaction of an agent with its tools, its environment, or other agents at scale. It…
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An AI agent capability maturity model is a conceptual framework that describes, level by level, how much an agent can do without human involvement, how errors are caught, and what conditions must hold for the system…
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Agentic reasoning is the process by which an AI agent decides what to do next, step by step, in order to reach a goal. It is what separates an agent from a standard language model response: instead of answering in…
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Mortgage brokerage runs on documents, deadlines, and rate windows. Missing one borrower item can delay a closing by weeks; a rate move you catch late costs a client real money. AI agents handle the operational layer,…
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Medical billing teams spend a significant portion of every day on work that is structured, repetitive, and rule-driven: scrubbing claims before submission, sorting denial codes, looking up modifier rules, and chasing…
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Law firm intake is where potential clients either become clients or leave for the next firm on their list. The quality of the intake experience, specifically how quickly the firm responds, how clearly it…
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Insurance agents spend a significant portion of their working week on tasks that are structured and repeatable: sending renewal notices, following up with new leads, collecting application documents, and updating…
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Architecture firms run on two parallel tracks: design work and coordination work. The design track is where principals earn their fees. The coordination track, proposals, spec updates, submittal logs, RFI chains,…
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Function calling and tool calling describe the same core behavior: a language model emits a structured request for an external capability, and the runtime executes it. The terms are often used interchangeably, but…
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Agent reflection is the process by which an AI agent evaluates its own output, identifies problems, and revises the result before returning it. It is how agents catch their own mistakes without requiring a human in…
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Long-term memory in an AI agent is any mechanism that stores information outside the active context window so it can be retrieved in a future session. Without it, an agent starts fresh on every run: no record of…
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LLM-based AI agents are probabilistic by default: the same input can produce different outputs on different runs. This guide explains where that variability comes from and the practical techniques teams use to…
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Staffing agencies win or lose on speed and throughput. The agency that sources a strong shortlist first, screens accurately, and moves candidates through the process without losing them to slow communication gets the…
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