How Kaer Agents Actually Work (Without the Buzzwords)

Every AI company loves to throw around terms like "reasoning," "planning," and "autonomous execution." Most of them don't explain what these words actually mean in practice. We'd like to be different.

Here's what actually happens, step by step, when you create a task on Kaer and click run.

Step 1: Understanding the goal

You write something like: "Research the top five project management tools for small teams, compare their pricing and key features, and write a recommendation for a 10-person startup."

The first thing the agent does is parse this into a structured goal. It identifies what you're asking for (a comparison and recommendation), what the subject is (project management tools), what the constraints are (small teams, 10 people), and what the deliverable should look like (a written recommendation).

This isn't keyword matching — the agent genuinely understands the intent. If you'd phrased it differently — "help me pick a PM tool for my small team" — it would arrive at the same understanding.

Step 2: Making a plan

Before doing anything, the agent creates a plan. For our project management research task, the plan might look like:

1. Search the web for recent rankings and reviews of PM tools for small teams
2. Identify the top 5 most recommended tools
3. For each tool, find current pricing information
4. For each tool, identify key features relevant to small teams
5. Compare the tools across a consistent set of criteria
6. Write a recommendation based on the comparison

This plan isn't rigid. If step 3 reveals that one of the tools has shut down or radically changed its pricing, the agent adjusts. If a search in step 1 surfaces a tool that wasn't in the original five but looks relevant, the agent considers adding it. The plan is a starting point, not a script.

Step 3: Using tools

This is where agents diverge from chatbots. Instead of generating a response from memory, the agent actually goes and does things.

For our research task, the agent might:

• Search the web for "best project management tools small teams 2026"
• Scrape the pricing pages of each identified tool
• Search again for recent reviews or comparisons specific to each tool
• Execute code to organize the data into a structured comparison table

Each tool use is a discrete action with a purpose. The agent decides which tool to use, what input to give it, and what to do with the output — all autonomously. If a web search returns unhelpful results, the agent reformulates the query and tries again. If a pricing page is behind a wall it can't access, it searches for the pricing information elsewhere.

Step 4: Synthesizing results

Once the agent has gathered enough information, it synthesizes everything into the requested deliverable. For our task, that means writing a comparison report with a clear recommendation.

The synthesis isn't just a copy-paste of search results. The agent weighs the evidence, considers the specific context (10-person startup), and arrives at a reasoned recommendation. It might note that Tool A is the cheapest but lacks a mobile app, while Tool B costs more but has the best integration ecosystem for small teams.

Step 5: Delivering the output

The finished result appears in your Kaer dashboard. You can review it, provide feedback, or send it downstream — to an email, a Slack channel, a webhook, or the next step in a workflow.

Depending on how you've configured the task, the agent might also:

• Send the report via email automatically
• Post a summary to a Slack channel
• Trigger the next node in a workflow pipeline
• Store the results for later reference

What about errors?

Things go wrong. A search API returns an error. A website blocks the scraper. The model generates something that doesn't match the requested format. Good agent systems — and we work hard to make Kaer one of them — handle errors gracefully.

Our agents use a few strategies:

Retry with variation. If a web search fails, try a different query formulation. If a scrape fails, try a different URL for the same information.

Fallback paths. If real-time data isn't available, use the most recent cached information and note the limitation in the output.

Graceful degradation. If one piece of a multi-part task fails, complete the rest and clearly note what's missing rather than failing the entire task.

Transparency. When the agent encounters limitations, it says so in the output. "I couldn't access Tool C's current pricing page. The pricing shown is from their most recent public announcement in January 2026."

Why this matters

Understanding how agents work isn't just academic. It helps you write better task descriptions (be specific about the deliverable), set better expectations (complex tasks take longer), and troubleshoot when results aren't what you wanted (was the goal clear? were the constraints explicit?).

The best agent users we've seen treat the agent like a capable colleague: clear about what they need, specific about constraints, and willing to iterate on instructions when the first attempt isn't perfect.