Learn how to build a targeted hiring signal agent in lemlist so you can track companies hiring for the roles and geographies that matter most to your outreach.
By the end of this tutorial, you’ll know how to create a hiring-based signal agent, refine it with inclusion and exclusion criteria, choose the scope to monitor, set a daily identification limit, understand that each company identified costs 100 credits, and decide how identified signals should be processed.
Why this matters
A hiring signal agent helps you spot companies that are actively investing in specific roles, which is often a strong buying signal. Instead of manually checking job boards, you can automate discovery and focus your outreach on accounts that are more likely to be ready for a conversation.
With the right inclusion and exclusion criteria, your signal agent stays focused on relevant, net-new opportunities and helps your team act faster on meaningful hiring activity.
Prerequisites
You should already know how to navigate the Signal agents section in lemlist.
You should already have a clear idea of the job titles, keywords, or locations you want to monitor.
You should already understand which companies, regions, or segments you want to include or exclude from tracking.
Core lesson — step-by-step workflow
Phase 1: Start a new signal agent
Go to Signal agents, then click Create signal agent to open the builder. This is where you define the type of signal you want lemlist to track for you.
In the Company growth category, select Company is hiring. This option helps you identify companies showing hiring intent for specific roles and geographies.
Phase 2: Configure the hiring signal
Enter a clear agent name so you can quickly recognize its purpose later.
Under Inclusion criteria, add the job titles and hiring geographies you want to track. You can also add hiring post keywords if you want to narrow results further.
Under Exclusion criteria, add any job titles, keywords, or geographies you want to filter out. Use exclusions to remove irrelevant regions or hiring signals that do not match your targeting.
Tip: Start with broader inclusion criteria, then use exclusion criteria to eliminate noise. This usually gives better results than making your inclusion criteria too narrow from the start.
Phase 3: Choose the scope and daily limit
Select the scope you want to monitor: All segments, Company list, or Specific segment. This determines whether lemlist should search broadly or only within a defined group of companies.
Set the maximum number of signals identified per day using the slider or preset options. This controls how many companies can be surfaced daily and helps you manage credit usage more predictably. Each company identified costs 100 credits, so your daily limit directly affects how many credits you can spend per day.
Note: Each identified company uses 100 credits, so it’s a good idea to start with a lower daily limit while you validate signal quality.
Phase 4: Decide how signals should be processed
Choose how you want identified signals to be handled. You can review them manually or automatically create tasks for follow-up. If your team works from task queues, selecting Auto-create tasks makes it easier to operationalize new opportunities quickly.
If you create tasks automatically, configure the task details such as task type, owner type, fallback owner, priority, title, and instructions so signals are routed clearly to the right person.
Phase 5: Review and confirm
Review the summary before finishing. Confirm the monitored signal, inclusion and exclusion criteria, billing impact, and signal processing method. This final review helps you catch mistakes before the signal agent goes live.
Practical application
Here’s a simple real-world example: imagine you sell sales enablement software and want to identify growing companies that may need support for new commercial hires. You could create a watchlist for companies hiring roles like Sales or Key Account Manager in the United States and France, while excluding Canada.
If you want to monitor broadly with All segments, you can also exclude a company list such as Current Customers or Do Not Contact. That way, your team only sees actionable signals from companies that are more likely to be net-new opportunities rather than accounts you already manage or want to avoid.
Keep in mind that each company identified uses 100 credits, so it’s worth starting with a manageable daily limit while you validate signal quality.
Troubleshooting & pitfalls
Issue: The signal agent returns too many irrelevant companies
Root cause: Your inclusion criteria may be too broad, or you may not be using exclusions effectively.
Narrow your monitored job titles.
Add hiring post keywords to improve relevance.
Use exclusion criteria to remove unwanted geographies or job-title patterns.
Use a more targeted scope such as a Company list or Specific segment if broad monitoring creates too much noise.
Issue: You’re not seeing enough signals
Root cause: The criteria may be too restrictive or the selected scope may be too narrow.
Expand your job title list with close variants.
Broaden geography filters.
Check whether you selected a very limited company list or specific segment.
Issue: Credit usage is higher than expected
Root cause: Your daily identification limit is set too high for your current workflow or budget. Since each company identified costs 100 credits, even a small increase in the daily cap can significantly raise credit consumption.
Reduce the daily signal cap in the identification limit section.
Start with a small daily number and increase it once quality is validated.
Review the estimated billing impact before finalizing the signal agent.
Issue: Signals are found, but no one follows up
Root cause: Signal processing may be set to manual, or task routing may not be configured clearly.
Select Auto-create tasks.
Assign an owner type and fallback owner.
Add clear instructions in the task configuration so reps know what to do next.