Agentic AI is moving beyond chat—these systems can plan, call tools, and take actions. Here’s a concise playbook to choose pilots, design the stack, and measure ROI safely.
What is Agentic AI (in plain English)?
Agentic AI pairs a large language model (LLM) with tools, memory, and a loop for planning and acting. Unlike a chatbot, it can execute tasks across apps with minimal supervision.
For context and trends, see Forbes’ coverage of agentic AI here.
Why it matters now
Agentic systems promise real productivity gains when scoped to narrow, repetitive workflows. Analysts expect significant economic impact from applied generative AI, especially in customer ops and software engineering.
See McKinsey’s macro view of generative AI’s potential here.
The agentic AI stack (reference)
- Planner (LLM): Chooses next step; supports function/tool calling.
- Tools: Company APIs, RPA endpoints, search, databases.
- Memory: Short-term scratchpad; long-term vector store for facts.
- Orchestrator: State machine/graph to constrain the loop.
- Verification: Rules, tests, or secondary models that check outputs.
- Observability: Traces, prompts, costs, latency, and replay.
- Policy & safety: PII filters, allowlists, rate limits, audit logs.
5 fast pilots you can ship in 30 days
- Email triage agent: Classify, draft responses, and route. KPI: first-response time, deflection rate.
- Sales research agent: Summarize accounts from CRM + web. KPI: prep time per meeting, coverage of key accounts.
- IT ticket resolver: Suggest fixes and update fields. KPI: mean time to resolution, auto-close rate.
- Invoice matching: Reconcile POs, flag exceptions. KPI: touchless rate, exception precision.
- Procurement intake: Turn free-text requests into clean PRs. KPI: cycle time, rework rate.
Guardrails that actually work
- Constrain scope: One task, one system of record, clear exit criteria.
- Tool allowlists: Only approved APIs with strict schemas.
- Sandbox first: Read-only runs; promote to write access after tests.
- Verifier step: Rules or a smaller model checks the agent’s output.
- Human-in-the-loop: Required approvals for high-risk actions.
- Ground truth: Retrieval or templates for factual or regulated content.
- Cost & time caps: Kill-switch on runaway loops; alerting on anomalies.
For risk guidance, see the NIST AI Risk Management Framework here.
Metrics that matter
- Task success rate: Percentage of tasks completed without rework.
- Deflection: Percent handled by the agent vs. humans.
- Time saved: Minutes reduced per task or per case.
- Cost per task: Model + infra costs vs. baseline labor.
- Quality score: Human review rating or policy compliance rate.
Build vs. buy: quick guidance
- Build if you need tight system control, custom tools, or sensitive data boundaries.
- Buy if you want fast time-to-value, bundled observability, and admin controls.
- Hybrid is common: off‑the‑shelf orchestration, custom tools and policies.
Starter blueprint (7 steps)
- Choose one workflow with clear inputs/outputs and low blast radius.
- Map tools and permissions; start read-only.
- Write the guardrails: policies, test cases, and stop conditions.
- Implement a simple plan-act-observe loop with schema-validated tools.
- Add a verifier and human approval for risky actions.
- Pilot with 10–20 users; log traces, costs, and errors.
- Graduate to write access; expand scope only after metric wins.
Sources
- Forbes: Agentic AI coverage forbes.com/topics/agentic-ai
- NIST AI Risk Management Framework nist.gov
- McKinsey: Economic potential of generative AI mckinsey.com
Takeaway: Start narrow, add guardrails early, and instrument everything. The gains come from repetitive workflows, not “general” autonomy.
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