What agentic AI is good at
Agentic systems are best understood as orchestration layers that decide what to do next, not as omniscient problem solvers. The most durable wins come from pointing small, reliable agents at clearly defined outcomes with crisp interfaces to the rest of your stack.
Start with a narrow bottleneck
“Handle support” is too broad. “Classify incoming tickets, draft answers using policy templates, and route exceptions for approval” is concrete. The narrower the scope, the faster you get to reliability and measurable impact.
Design the unit of work
An agent should accept a well-typed input, call a small number of tools, and emit a structured output. That output should be easy to verify automatically. For example: given a document, return a JSON object with classification, confidence, and citations. The citation requirement alone raises quality.
Build guardrails as data
Policy prompts, schema definitions, and validation rules should live alongside your agent so they can evolve without deployments. Schemas double as both contract and constraint; when the model strays, validation catches it and a retry with more guidance brings it back.
Put humans where they add value
There are three leverage points: approvals for high-risk actions, exception handling when confidence is low, and periodic sampling for quality control. A simple approval queue with reason codes creates a feedback loop your agents can learn from.
Patterns that improve reliability
Agents that call deterministic tools (search, retrieval, calculations, APIs) are more consistent than agents trying to reason everything out in free text. Compose tools as steps and let the model pick the order, not the implementation.
Measure the right outcomes
Track latency, success rate, and override rate. If your success rate climbs while overrides drop and latency stays within UX budgets, you are scaling a healthy agent. If overrides stay high, revisit the unit of work and the quality of your tools before changing models.
Keep the event stream
Emit structured events at each step: inputs, prompts, tool calls, outputs, and decisions. When something goes wrong, the fastest path to a fix is a replayable trace. This is also how you teach new agents; mine successful traces and generalize.
Ship with feature flags
Agents should be easy to disable per customer, per workflow, or per action. Progressive rollout beats big-bang launches, and flags let you target cohorts where the agent’s payoff is clearest.
Operate the system over time
Upstream app changes, new edge cases, and model updates will shift behavior. A weekly cadence of sampling, evaluation runs, and small adjustments is cheaper than infrequent refactors. Treat agents like products: iterate in small, safe steps.
Bottom line
The ROI comes from turning previously manual micro-decisions into fast, consistent flows with transparent oversight. When agents are boring, they are working.
Implementation checklist
- Define the unit of work and success criteria
- Provide deterministic tools with clear contracts
- Add schema validation and retries
- Capture events and enable replay
- Add approvals for high‑risk actions
- Start with a small rollout behind flags
Common pitfalls to avoid
- Prompting your way around missing tools
- Launching without an approval or exception path
- Ignoring event traces until something breaks
- Expanding scope before nailing a single workflow
