1. Competitive price monitoring
Set the agent to visit your top 3-5 competitor pricing pages every Monday morning, extract the pricing tiers and included features, compare against a stored baseline, and send you a summary of what changed. If a competitor dropped their price or added a feature to a lower tier, you know before the week starts.
This works because competitor pricing pages load reliably, follow consistent HTML patterns the agent can learn to extract from, and the comparison logic is simple enough that errors are obvious. Setup time: write the initial instruction with the target URLs and what data to extract. The agent handles the rest.
More advanced version: add a step that pulls recent reviews from G2 or Trustpilot mentioning price, finds the count of reviews that cite pricing as a concern, and includes that in the weekly brief. This gives you both the objective pricing data and the customer sentiment signal.
2. Morning research brief
Schedule an agent to run at 6am every weekday. It browses a set of sources you define — specific publications, subreddits, HackerNews, newsletters via RSS — extracts the items relevant to your work based on criteria you define, and sends you a formatted summary before you sit down. This replaces 30-45 minutes of manual tab-opening with a curated digest that arrives when you want it.
The key to making this useful is being specific about what counts as relevant. "AI news" is too broad. "New open-source agent frameworks or major model releases" is a reasonable scope. The more precise your criteria, the better the signal-to-noise ratio.
The agent improves at this over time. As it learns which sources you actually read versus skim past, it can weight future briefs accordingly.
3. Email triage
Connect the agent to your email account via the Gmail API or IMAP. Set it to check every few hours, classify incoming email by urgency and category (customer inquiry, invoice, newsletter, support request, sales outreach), draft responses for the high-frequency low-complexity items, and flag the ones that need your direct attention.
The draft responses for common inquiries — pricing questions, support confirmations, meeting scheduling — are the highest-leverage part. The agent generates the draft, puts it in your drafts folder (or a staging folder), and you review and send. This is not full automation; it is acceleration. A 30-second review of a well-drafted response beats writing it from scratch.
This requires giving the agent access to your email, which requires thought about what you are comfortable with. Start with read-only access and draft generation. Move to send access only after you are confident in the output quality.
4. Weekly codebase review
This one is for teams. Schedule the agent to pull the diff from the past week's merged PRs, look for patterns that match your known technical debt categories (raw SQL queries, missing error handling, test coverage gaps, deprecated dependencies), and generate a brief report with links to specific files and line numbers.
The agent does not replace code review — it prepares for it. Having a weekly summary of "here are the three files that got the most churn this week, and here are the two that still have raw SQL" gives reviewers a useful starting point.
This works best if you spend an hour initially defining the categories you care about and showing the agent examples of both problems and acceptable patterns. The upfront investment makes the ongoing output useful rather than noisy.
5. Lead enrichment
When a new lead comes in — via form submission, LinkedIn connection, cold email, whatever your source is — the agent can look up publicly available information about the person and company, pull data from LinkedIn, the company website, and news, and populate a structured record in your CRM or a Notion database.
The value is not replacing research; it is doing it automatically before the lead goes cold. A sales rep who opens a lead record and already has company size, recent news, and mutual connections has a better first call than one who has to look all that up manually.
This requires configuring the integration between your lead source (Typeform, a webhook, a Google Sheet) and the agent's trigger mechanism. It is more setup-heavy than some of the other examples here, but the time savings per lead compound quickly.
6. Content repurposing
You publish a long-form piece — an article, a technical post, a deep-dive thread. The agent reads it, extracts the key arguments, and generates three LinkedIn posts with different angles, a tweet thread, and a plain-language summary version. All go into a staging folder for your review before publishing.
The output quality varies — for anything requiring your specific voice and opinions, the drafts are starting points more than finished pieces. But for factual summaries, quote extractions, and structural repurposing, the agent saves 60-90 minutes per piece.
Over time, as the agent builds a model of your writing style and which posts performed well, the drafts get better. This is a task where persistent memory pays off clearly: the agent that has seen 50 of your posts writes better drafts than one starting from scratch.
7. Incident monitoring
For developers: schedule the agent to check your error monitoring (Sentry, Datadog, Grafana) every hour, group recurring errors by type and frequency, and alert you on Telegram when error rates cross a threshold or a new error class appears for the first time.
This is not a replacement for proper alerting infrastructure, but it adds a layer that generic monitoring tools miss — the agent can look at the error context, search the codebase for the relevant section, and include in the alert a brief description of what part of the system is involved. A human still responds, but with more context than a raw stack trace.
For higher-volume workloads: Hermes v0.5.0 supports batch processing — running the agent across hundreds or thousands of prompts in parallel, outputting structured ShareGPT-format trajectory data. This is used for training data generation and evaluation pipelines, not just operational monitoring. The parallel subagent system (up to 3 concurrent subagents, each with their own isolated context and terminal session) handles concurrent monitoring streams without blocking each other.