What Are Agents?
In our last post, we explored traditional workflows (structured, rule-based systems that automate repeatable processes). While effective for predictable tasks, workflows rely heavily on pre-defined paths.
But what happens when a task requires judgment, adaptation, or goal-seeking behavior?
That’s where agents come in.
What Is an Agent?
In the tech world, an agent is a system that can perceive its environment, interpret goals, and autonomously decide which actions to take next. Unlike workflows, agents aren’t limited to a single, pre-written sequence of steps.
Instead, they respond to real-world inputs, adjust plans in real-time, and can select tools or strategies based on context.
Think of an agent as a digital co-pilot, not just a task runner, but a task thinker.
Core Characteristics of Agents
1. Goal-Oriented
Agents are typically given an objective, not a strict script.
Example:
“Find a good time to schedule a candidate’s interview.” The agent determines how to do that based on the calendar, response history, and preferences, not by following a rigid checklist.
2. Context-Aware
Agents perceive and interpret signals from their environment:
- Calendar availability
- Email reply patterns
- Tool usage data
- Past behaviors or outcomes
This allows them to adapt to dynamic situations rather than break down when something unexpected occurs.
3. Autonomous Decision-Making
Where workflows follow predefined rules, agents make decisions on the fly.
An agent might:
- Choose the best tool for the task
- Retry a failed step with a different strategy
- Escalate an issue if it recognizes something beyond its scope
4. Iterative and Self-Correcting
Agents often operate in loops:
- Observe the environment
- Plan an action
- Execute the action
- Assess the result
- Adjust or try again
This makes them particularly well-suited to ambiguous tasks where the next best action is not always obvious.
A Simple Agent Example: Scheduling Interviews
Let’s look at a simple interview scheduling scenario, powered by an agent:
- The agent receives a request: “Schedule with Taylor and Alex next week.”
- It checks calendars and recent replies to determine availability.
- It drafts personalized emails based on candidate history.
- If someone doesn’t respond in 48 hours, it retries with a different time or escalates.
- It updates your ATS and calendar once confirmed.
There’s no fixed script, just a goal and the intelligence to pursue it.
Strengths of Agents
- Adaptability: Handle unexpected changes or incomplete info
- Contextual awareness: Adjust actions based on real-time data
- Efficiency: Reduce human oversight for more complex tasks
- Goal-driven: Focus on outcomes, not just tasks
Limitations and Considerations
- Harder to predict: Outputs may vary across runs
- Observability: It can be challenging to trace decisions
- Trust & control: Requires guardrails to ensure reliability
- Complex to build: Needs orchestration and safe tool use
Where Agents Fit
Agents are especially useful when:
- The task involves multiple tools and uncertain outcomes
- The process can’t be neatly mapped as a flowchart
- The system needs to reason or explore different options
- Human intervention isn’t scalable for every edge case
Examples:
- Smart scheduling and coordination
- Document summarization or data retrieval
- Workflow triage and optimization
- Knowledge work automation (e.g., talent research, vendor vetting)
Snow Owl leverages agentic capabilities to handle exactly these kinds of dynamic, context-dependent scenarios across numerous departments, bringing autonomy into places where strict workflows fall short.
It’s not just about executing tasks—it’s about making informed, real-time decisions that improve results. Snow Owl’s agent-driven features help hiring teams move faster, handle exceptions gracefully, and keep processes moving without constant human intervention.
Up Next:
Post 3: Comparing Structure - Determinism vs. AutonomyNext week, we’ll directly compare how workflows and agents differ in architecture, behavior, and reliability.