The Promise and Peril of Agentic Systems
Agentic workflows are compelling: instead of hardcoding every branch in your automation, you let an LLM decide what to do next. The agent can call APIs, search databases, draft responses, and chain multiple steps together without explicit programming. It feels like magic—until it doesn't.
The challenge is reliability. Traditional RPA (robotic process automation) follows strict rules. If the input matches pattern X, execute action Y. It's predictable, auditable, and safe. Agents, by contrast, are probabilistic. They might choose a different path on the same input twice. For low-stakes workflows, that's fine. For high-stakes decisions—approving expenses, modifying records, escalating support tickets—it's a risk.
Where Agents Add Real Value
Agents shine in scenarios where the workflow changes frequently or requires contextual judgment. Triaging customer support tickets, for example: the agent reads the message, checks the customer's history, searches the knowledge base, and either drafts a response or routes to a specialist. The logic is too variable to hardcode, but too important to ignore.
Another strong use case: research and summarization tasks. An agent can search multiple sources, extract relevant information, cross-reference data, and synthesize a report. The human reviews and approves before it goes anywhere. The agent handles the tedious work; the human provides the judgment.
The key insight: agents work best when they augment human work, not replace it entirely. They gather information, draft options, and surface recommendations. The human makes the final call.
Building Safe Agentic Systems
If you're deploying agents in production, start with guardrails. First, scope the agent's permissions tightly. It should only access the APIs and data it absolutely needs. Use read-only access wherever possible. Any write operation should require explicit approval.
Second, add checkpoints. If the agent is about to take a high-cost or high-risk action—sending an email, updating a database, charging a customer—pause and ask for human confirmation. You can set confidence thresholds: if the agent is less than 90% confident, escalate to a person.
Third, log everything. Every decision the agent makes, every API it calls, every piece of data it retrieves. You need an audit trail for debugging, compliance, and continuous improvement. Review logs weekly. Look for patterns where the agent made poor choices. Refine the prompt or add constraints.
When to Use Traditional Automation Instead
Not every workflow needs an agent. If the logic is deterministic—"if invoice total exceeds $10,000, route to finance director"—use a rule. Rules are faster, cheaper, and more predictable than agents. Save the LLM for cases where you genuinely need reasoning or natural language understanding.
Hybrid approaches work well. Use rules for the deterministic steps, then hand off to an agent for the ambiguous ones. For example: validate the form with rules, then use an agent to assess whether the request justification is reasonable. Best of both worlds.
Monitoring and Cost Control
Agents can get expensive fast. Each step might involve multiple LLM calls, tool invocations, and retrieval operations. Monitor cost per task. If a workflow is running 1,000 times a day and each run costs $0.50 in API fees, you're spending $15,000/month. That might be fine—or it might be a signal to optimize.
Introduce caching for repeated tasks. If the agent searches the same knowledge base article 50 times in a day, cache the result. Use smaller models for simple steps and reserve the large models for complex reasoning. Batch API calls where possible.
Measure latency, too. If the agent takes 45 seconds to respond, users will abandon it. Set timeouts. Optimize the critical path. Agentic workflows are powerful, but they need engineering rigor to work at scale.