Building autonomous AI agents. Notes from the team building Gravity. AI workflows, the future of recurring work, and what we learn along the way.
Yes, an AI agent can run PandaDoc proposal follow-up end to end: it watches each proposal for status changes and open events through the PandaDoc API, sends timed and context-aware follow-ups, alerts the rep the…
Read post →
Yes, an AI agent can triage your Outlook inbox: reading each incoming message, classifying it by sender, topic, and intent, then prioritizing what needs a reply, routing it to the right folder or category, and…
Read post →
Yes, an AI agent can draft your monthly investor update: pulling the recurring metrics from their sources, assembling the standard sections, and writing the narrative in your voice from a template, while flagging…
Read post →
Yes, an AI agent can run the Gusto onboarding flow for each new hire: when someone is added, the agent opens the onboarding checklist, chases the missing documents and signatures, reminds managers to provision…
Read post →
Yes, an AI agent can track DocuSign contracts end to end: monitoring each envelope's status, chasing signers who have not signed, alerting you when an agreement stalls or a deadline approaches, logging completed…
Read post →
Yes, an AI agent can turn a Datadog alert into a usable incident summary: when a monitor or incident fires, the agent pulls the triggering alert, the relevant metrics around the event window, and the related log…
Read post →
An AI agent can take board meeting prep from a multi-day scramble down to a reviewable draft. It gathers the recurring inputs that go into every deck, financials, KPIs, pipeline, hiring, and product progress, from…
Read post →
Yes, an AI agent can watch an Amplitude conversion funnel for you: pulling step-by-step conversion rates through the Amplitude API, comparing each step against its recent baseline, and alerting the right owner the…
Read post →
The short answer The short answer 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…
Read post →
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…
Read post →
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:…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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,…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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…
Read post →
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,…
Read post →