AI Agents: Start Simple, Then Add Complexity — Our Recommended Path

AI agents can sound intimidating especially if your experience with AI has been limited to interacting with chatbots like ChatGPT, Gemini, or Claude. Depending on how you've heard them presented, they can even sound unsettling, and dystopian. In practice, agents are neither science fiction nor replacements for people. They are a way to produce value through software workflows that have historically required human-level judgment and attention in the pre-LLM era. This is especially true with today's reasoning models (like OpenAI's o3 or Claude Opus 4.5), which are designed for multi-step planning and tool use.

The value in agents isn’t about autonomy for its own sake.
It’s about applying AI practically.

A Simple Definition

An AI agent:

• Has a clearly defined goal
• Uses an AI model to help decide next steps, including interfacing with tools and data sources
• Can take actions in real systems (with permission)
• Observes conditions and operates within guardrails you control

Unlike a chatbot, an agent doesn’t just respond.
Unlike a script, it doesn’t require every scenario to be preprogrammed.

Instead, it combines deterministic software (rules and tests that behave the same every time) with AI judgment where flexibility and prioritization matter.

How Agents Differ from other AI or Automation Software

It helps to ground this in tools you already know.

Chatbots

Chatbots take text in and return text out. They don’t have a persistent objective, and they don’t take a variety of actions besides just responding to you.

Traditional automation (scripts, cron jobs)

Scripts are excellent when rules are clear and outcomes are predictable. They do exactly what they’re told, and nothing more. When reality gets messy, they fail unless a human updates the logic. The truth is they have historically required lots of trial and error to fine-tune software. That still happens with agents, but with the autonomy we imbue them with, they can accelerate improvement cycles. This lets you rely on AI judgment for edge cases while rapidly refining the deterministic rules.

AI agents

Agents are designed for situations where:

• Inputs are incomplete or ambiguous
• There isn’t one “right” answer, only better or worse ones
• Work spans multiple tools or systems

They don’t replace deterministic logic. They sit alongside it, filling in judgment gaps when rules alone fall short.

The Agent Loop

Most agents follow a simple pattern:

1. Observe the current situation
2. Decide what to do next
3. Act using an approved tool
4. Observe the result
5. Repeat until done or stopped

This loop only runs within boundaries you define. In production systems, agents are intentionally constrained, especially when actions affect important business processes or sensitive information.

Understanding how this loop behaves in practice is one of the main reasons we recommend starting with low-stakes use cases.

A well-designed agent doesn’t run forever. It either completes the task, hits a safe limit (time/steps/cost), or stops and asks for help when it’s uncertain.

What Makes an Agent “Practical”

Practical agents share a few traits:

• Clear scope: One, simple job
• Bounded autonomy: Freedom only where risk is low
• Human oversight: Approval where mistakes are costly
• Deterministic backbones: Tests, rules, and checks do the heavy lifting
• Transparency: Every action is logged and reviewable for learning and improvement

This approach avoids two common failure modes:

• brittle automation that breaks silently
• unbounded AI that does too much, too fast

Where Agents Add Real Value

Agents are not valuable because they’re sophisticated.
They’re valuable when the judgment, automation or efficiency they provide outweigh the operational complexity they introduce.

In practice, we see the strongest results in three categories.

1. Ongoing operational work

Tasks that never stop but don’t justify constant human attention:

• monitoring for changes
• checking for issues
• preparing work for review

2. Judgment-heavy workflows

Processes where prioritization and tradeoffs matter:

• assessing risk
• choosing between acceptable options
• flagging anomalies

3. Cross-tool coordination

Work that spans systems and context:

• CMS + testing + version control
• content + taxonomy + analytics
• tickets + documentation + notifications

These are exactly the places traditional automation struggles, and this is where agents can help if the value is there.

Safety, Guardrails, and Control

What happens when an agent makes a mistake? You should absolutely expect them to fail in nearly every way possible. Be pessimistic with your expectations at the outset; and log everything. it will reduce frustration and accelerate learning.

In well-designed systems:

• Agents can only see the data you expose
• They can only use the tools you explicitly allow
• High-impact actions require human approval
• Environments are isolated (staging vs production)
• Every step is logged for audit and rollback
• Failure is assumed as a norm, and accounted for preemptively

Autonomy in production isn’t assumed. It’s earned gradually through testing, observation, and trust, and only to the level where some potential failure is acceptable.

Our Core Recommendation

Agents introduce real power, but they also introduce real complexity, and cost.

Our recommendation is straightforward: start with low-stakes, well-bounded use cases to become familiar with agentic workflows, and only apply agents to more complex processes when the value clearly justifies that added complexity.

Why “Low Stakes First” Works

One practical benefit of agents is that they can lower the barrier to getting useful results rapidly. Instead of needing to predict every edge case up front, you can start with clear goals and guardrails, observe how the workflow behaves, record performance, and then “harden” the predictable parts into deterministic rules over time.

In other words: agents can help you get to value sooner, and then you make the system more reliable as they learn, in some cases, completely exiting the agentic workflow in favor of deterministic software that operates predictably on every run.

Where We Started: A Deliberately Low-Stakes Slack Agent

Before applying agents to higher-value or higher-risk workflows, we started with something intentionally small.

Our first agent lived entirely inside Slack. Its job was simple: help manage a lightweight internal rotation (who should present, facilitate, or go next) based on a few basic constraints.

If it made a mistake, the cost was trivial. At worst, someone got pinged twice or had to swap with a teammate. No customers were affected. No systems were modified. No irreversible actions were taken.

That was the point.

This Slack agent gave us a safe environment to observe how agentic workflows behave in practice: how models reason step-by-step, where they struggle, how often humans need to intervene, and what kinds of guardrails actually matter.

Importantly, we didn’t try to make it “perfect.” We let it run, watched where it failed, and then gradually added structure (clearer rules, better context boundaries, and explicit checks) where patterns emerged.

Even with passing unit tests, the randomization sometimes produced results that didn't feel 'fair' to the team, which is exactly the kind of edge case you want to catch in a low-stakes environment. Because results posted to Slack (meta), we spotted these quickly and refined the logic.                                              
 

Taking Next Steps After Low-Stakes Familiarization

These ideas aren’t theoretical. They’re how we approach Practical AI at Savas Labs.

We intentionally start with low-stakes use cases to understand how agentic workflows behave in real environments. From there, we apply agents only where the payoff clearly outweighs the added complexity.

You can see how this philosophy shows up in some products we've begun to roll out in solutions we’re building at https://savaslabs.com/solutions.

A few examples:

Keeping Systems Healthy and Secure

CMS Patch Pilot
Security updates for platforms like Drupal and WordPress are essential, but applying them safely is time-consuming and risky if rushed.

This solution:

• Monitors for relevant security updates
• Applies patches in staging environments first
• Runs checks to catch regressions
• Prepares review-ready changes for human approval

Nothing is deployed automatically to production.

Outcome: Faster patch cycles, fewer surprises, and reduced operational load.

Here, the complexity is justified by risk reduction and operational value.

Improving Content Quality at Scale

Website Content Optimizer
Routine content audits are valuable, but often deprioritized.

This solution:

• Crawls your site and runs deterministic checks
• Uses AI judgment to assess clarity, tone, and alignment with goals
• Stages improvements for easy human review

Outcome: Higher-quality content without automatic publishing or loss of control.

Content Tag & Taxonomy Auditor
Disorganized taxonomies quietly degrade search and navigation.

This solution:

• Maps tag usage and redundancy
• Evaluates structure against best practices
• Suggests improvements for review before any changes

Outcome: Cleaner structure and better discoverability.

Reducing Operational Friction

Workflow Assistants
Some of the most valuable applications are also the least glamorous.

We’ve built agents to assist with:

• aggregating signals from recruiting applications
• invoice reconciliation
• document quality evaluation and submission workflow
• marketing email auto-unsubscriber
• trip planning
• financial reporting

These low-stakes workflows are often where teams first become comfortable with agentic systems, before applying them to higher-value processes.

Tools That Improve Over Time

These solutions aren’t static products.

As we continue to build:

• guardrails become more precise
• evaluations become more comprehensive
• defaults improve based on real usage

The pace to build has unlocked the opportunity to save time and improve quality with custom software that was previously out of reach, and the ability for more reporting and rapid improvement is exciting.

How to Explore Agents in Your Organization

If you’re considering agents, a good starting point is simple:

• Identify a low-stakes workflow
• Define what “done” looks like
• Decide what must remain deterministic
• Specify where human approval is required

From there, you can evaluate whether the added judgment is worth the added complexity.

See our agents in action at with our solutions, and reach out if we can help you apply agents to your workflows.