Agentic Workflow Management

From Prompts to Processes

Most AI usage today is prompt-based — a human types something, an AI responds, and the human decides what to do next. Agentic workflow management is fundamentally different. It's about designing processes where AI agents autonomously execute multi-step operational work with minimal human intervention.

The shift isn't just technical. It changes how you think about work itself — from "tasks a person does" to "outcomes a system produces."

What Makes a Workflow Agentic

Not every automated process is an agentic workflow. A script that sends emails on a schedule is automation. A rule-based system that routes tickets is automation. An agentic workflow has three distinguishing characteristics:

Autonomous decision-making. The agent encounters situations that weren't explicitly programmed and uses reasoning to determine the next action. When a credential verification agent finds an expired license, it doesn't just flag it — it checks renewal status, estimates processing time, and determines whether the nurse can still be placed at facilities with grace period policies.

Dynamic task decomposition. The agent breaks complex goals into subtasks at runtime, not at design time. Given the objective "prepare this nurse for placement at Regional Medical Center," the agent identifies what credentials are needed, which ones are current, what gaps exist, and what actions to take for each gap — all based on the specific situation.

Environmental awareness. The agent reads from and writes to real systems — databases, APIs, scheduling platforms, communication tools. It doesn't just generate text. It takes action.

Designing Agentic Workflows

Start With the Outcome

The most common mistake is starting with what the agent should do step by step. Instead, start with the outcome you need. "Every nurse placed at a facility has verified, current credentials that match that facility's requirements." The agent's workflow emerges from that outcome — not from a prescribed sequence.

Map Decision Points

Walk through the current manual process and identify every point where a human makes a judgment call. These are your candidate agent decision points. For each one, ask:

• Is the decision based on information the agent can access?
• Are the decision criteria definable, even if complex?
• What's the cost of the agent making a wrong decision here?
• Can the decision be reversed if wrong?

High-information, definable-criteria, low-cost, reversible decisions are ideal starting points for agentic automation. Decisions that are ambiguous, subjective, high-stakes, or irreversible should stay with humans — at least initially.

Define Boundaries

Every agentic workflow needs clear boundaries:

• Scope boundaries: What the agent can and cannot do. A shift matching agent should not be able to modify facility contracts.
• Authority boundaries: What actions require human approval. Placing a nurse at a standard shift might be autonomous; overriding a credential gap requires a supervisor.
• Resource boundaries: How much compute, time, and cost the agent can consume before escalating.

Without explicit boundaries, agents either do too little (requiring constant human intervention, defeating the purpose) or too much (taking actions that create risk).

Implementation Patterns

Event-Driven Workflows

The most robust agentic workflows are event-driven rather than scheduled. Instead of running a credential check every hour, the system triggers a check when a new document is uploaded, when a placement is requested, or when an expiration date approaches.

Event-driven design means agents only work when there's work to do, reduces wasted computation, and creates natural audit trails — every action traces back to a triggering event.

State Machines with Agent Intelligence

A useful pattern combines traditional state machines with agent reasoning at each state transition. The workflow has defined states (document received, under review, verified, flagged, expired) and the transitions between states are handled by agents that reason about what to do, rather than simple rule-based logic.

This gives you the predictability and visibility of a state machine with the flexibility of agentic reasoning at each step.

Human-in-the-Loop Checkpoints

Fully autonomous workflows are rarely the right goal. Design explicit checkpoints where a human reviews the agent's work before it proceeds. Over time, as confidence in the agent's decisions grows, these checkpoints can be relaxed or removed.

The key is making these checkpoints non-blocking where possible. The agent can continue with other work while waiting for human review, rather than the entire workflow stalling.

Monitoring and Observability

Agentic workflows are harder to monitor than traditional automation because the agent's behavior isn't fully predetermined. You need visibility into three layers.

Decision Logging

Every decision the agent makes — and why — should be logged. Not just the outcome, but the inputs it considered, the alternatives it evaluated, and its reasoning. This is essential for debugging, auditing, and building trust.

Performance Metrics

Track both efficiency metrics (time to completion, throughput, cost per action) and quality metrics (decision accuracy, escalation rate, error rate). The ratio of autonomous completions to human escalations is a particularly useful indicator of workflow maturity.

Drift Detection

Over time, the distribution of inputs the agent encounters may shift. A credential verification agent trained on nursing licenses may start receiving physician credentials it wasn't designed for. Monitor input distributions and flag when the agent is operating outside its designed parameters.

Scaling Considerations

Workflow Versioning

As you improve agent logic, you need to handle in-flight workflows that started under the old version. Design your system so workflows lock to the version that started them, and new logic only applies to new workflow instances.

Graceful Degradation

When an agent fails or an external system is unavailable, the workflow should degrade gracefully — queuing work for later, falling back to simpler logic, or escalating to humans — rather than failing entirely.

Cost Management

Agentic workflows consume LLM inference on every decision. At scale, this adds up fast. Design workflows to use the minimum model capability needed at each step. A simple classification might use a small, fast model, while complex reasoning uses a more capable one.

What We've Learned

Building agentic workflows for Kapwa and our Strategy Analyzer has taught us several things:

• The 80/20 rule applies aggressively. The first 80% of a workflow is straightforward to automate. The last 20% — edge cases, exceptions, ambiguous situations — takes 80% of the effort. Design for this from the start.
• Agents need guardrails, not rails. Overly prescriptive workflows negate the benefit of using agents. Define boundaries and objectives, then let the agent reason about how to achieve them.
• Trust is earned incrementally. Start with low-stakes decisions, prove reliability, then expand scope. No organization should go from manual processes to fully autonomous workflows in one step.

These principles guide how we build, and we'll continue refining them as we ship more products and encounter new challenges.