Building autonomous AI agents. Notes from the team building Gravity. AI workflows, the future of recurring work, and what we learn along the way.
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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SEO agencies spend a large portion of each month on work that is structured and repeatable: running audits, pulling rank data, building client reports, researching keywords, and monitoring competitors. AI agents…
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SaaS customer success teams are expected to do more with fewer touches: protect net revenue retention, accelerate time-to-value for new accounts, and surface expansion opportunities, all while the book of business…
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PR agency work is 60 percent research and tracking, 40 percent relationship and strategy. Every pitch requires finding the right journalist, reading their recent coverage, personalizing the hook, and then monitoring…
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Running an Etsy shop means doing the creative work and the shop operations yourself, usually at the same time. Every order needs a confirmation message, every shipped package needs a tracking update, every listing…
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AI agents for dropshippers handle the operational work that scales badly with humans: routing orders to suppliers, syncing tracking numbers, sending customer updates, and monitoring supplier stock and prices. You…
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Solo digital marketing freelancers carry an unusually wide operational load: they do the client work, generate the reports, write the proposals, chase the invoices, manage the content calendar, and keep tabs on three…
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AI agents do not write your copy for you. They handle everything that has to happen before you write and after you submit, so you can spend more of your day doing the work that only you can do. Research, intake,…
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Running an Amazon business means managing a constant stream of operational tasks: listings that drift out of rank, inventory levels that creep toward zero, buyer messages that need a reply within 24 hours, and Seller…
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Three open protocols are competing to become the connective tissue of agentic AI systems: MCP, A2A, and ACP. Each solves a different interoperability problem, and understanding the distinction determines whether you…
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The short answer: LangSmith is LangChain's developer observability, tracing, and evaluation platform for LLM applications that engineers build and maintain in code. Gravity is an AI agent platform where…
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