Agentic AI is the evolution from “chat with an AI” to “delegate a goal and let the system execute.” Instead of only generating answers, an agentic system can plan steps, call tools (APIs, databases, ticketing systems), observe results, and iterate until it reaches an outcome. For engineering and DevOps teams, this unlocks automation that feels closer to a junior operator that follows instructions than a chatbot that only talks.
This guide explains what agentic AI is, the best real-world use cases, and how easy it is to build safely.
What is agentic AI
Agentic AI refers to AI systems designed to pursue goals through a loop:
- Understand the goal and constraints
- Plan a sequence of actions
- Execute actions via tools
- Observe outputs and update state
- Continue until success or a stop condition is met
The key idea is tool use. A normal model produces text. An agent produces progress by calling functions that you control, such as “search logs,” “create Jira ticket,” “fetch cloud costs,” or “open pull request.”
What agentic AI can be used for
1) Engineering delivery and productivity
- Turn a feature request into a task breakdown with acceptance criteria
- Draft a rollout plan and create the tickets automatically
- Audit a repo for missing runbooks, missing alerts, and release risks
2) DevOps and platform operations
- Incident triage: pull metrics and logs, correlate recent deploys, suggest likely causes
- Change assistance: generate a safe change plan, open a change request, and prepare rollback steps
- Cost hygiene: detect idle resources and produce a prioritized savings plan
3) Security and compliance support
- Evidence collection for audits: gather logs, configs, and access changes into a report
- Alert summarization: convert noisy detections into “what happened, impact, next actions”
- Ticket enrichment: add asset context, severity rationale, and recommended playbooks
4) Internal tech operations
- Procurement and vendor workflows: compare options, extract clauses, draft recommendations
- Support engineering: run diagnostics, reproduce common issues, draft clear response steps
A good agentic AI project is multi-step, tool-driven, and measurable. You should be able to define what “done” means.
How easy is it to build
A simple agent is easy. A reliable and safe agent is where the work is.
Easy parts
- Adding tool calling (functions the model can request)
- Writing a plan-act-observe loop
- Keeping state in memory or a small store
- Running the agent locally
Hard parts (the ones that matter)
- Safety and permissions (least privilege, read-only tools first, gated writes)
- Predictability (clear stop conditions, max steps, retries)
- Observability (logs of every tool call and result)
- Evaluation (tests for happy path, failures, and adversarial inputs)
- Guarding against prompt injection and tool misuse
If you keep the scope narrow and restrict tools, you can build something useful quickly.
A practical, safe build approach
Step 1: Pick a narrow use case
Start with read-heavy workflows. Examples:
- “Summarize incidents into a postmortem draft”
- “Generate a weekly cloud cost report and highlight anomalies”
- “Review Terraform plan output and list risks”
Step 2: Design tools like a strict API
Tools should be:
- Single purpose
- Well-typed inputs and outputs
- Easy to validate
- Hard to misuse
Avoid “do everything” tools. Prefer fetch_metrics() over manage_infra().
Step 3: Control the blast radius
- Separate read tools from write tools
- Require approval for write actions
- Scope credentials tightly and rotate them
- Rate limit actions and enforce budgets (time, cost, number of calls)
Step 4: Use a workflow-first pattern before full autonomy
Workflow-first agents follow a known sequence with small decision points. They are easier to test and safer to deploy. Later, you can add more autonomy in specific steps when you trust the behavior.
Step 5: Add logging and evaluation early
Log every step:
- user goal
- agent plan
- tool calls and tool outputs
- final result
- failures and retries
This becomes your debugging and improvement engine.
Common pitfalls
- Too broad goals (“optimize our cloud” is not a first agent)
- No stop conditions (agents can loop)
- Too much write access too early
- No audit trail
- Treating outputs as truth instead of validating with source-of-truth systems
References (URLs)
- https://www.ibm.com/think/topics/agentic-ai
- https://cloud.google.com/discover/what-is-agentic-ai
- https://platform.openai.com/docs/guides/function-calling
- https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
- https://docs.langchain.com/oss/python/langgraph/workflows-agents
Python example: a minimal, safe agent with tool calling
from __future__ import annotations
import json
import time
from dataclasses import dataclass
from typing import Any, Callable, Dict, List
# ----------------------------
# 1) Example tools (read-only)
# ----------------------------
def get_cpu_utilization(service: str) -> Dict[str, Any]:
"""
Replace this with a real metrics call (Prometheus, CloudWatch, Datadog, etc.)
"""
return {"service": service, "cpu_p95": 72.5, "cpu_avg": 41.2, "window": "24h"}
def search_runbook(query: str) -> Dict[str, Any]:
"""
Replace this with your real runbook search (Confluence, Notion, internal wiki, etc.)
"""
return {
"query": query,
"results": [
{"title": "Service Overload Playbook", "url": "https://intranet/runbooks/overload"},
{"title": "High CPU Investigation", "url": "https://intranet/runbooks/high-cpu"},
],
}
def list_recent_deploys(service: str) -> Dict[str, Any]:
"""
Replace this with your deploy source (GitHub, GitLab, ArgoCD, Spinnaker, etc.)
"""
return {
"service": service,
"deploys": [
{"sha": "a1b2c3d", "time": "2026-01-10T18:10:00Z", "summary": "Bumped dependency versions"},
{"sha": "d4e5f6g", "time": "2026-01-09T15:40:00Z", "summary": "Enabled caching layer"},
],
}
# Strict allow-list of tools the agent is allowed to call
TOOL_REGISTRY: Dict[str, Callable[..., Dict[str, Any]]] = {
"get_cpu_utilization": get_cpu_utilization,
"search_runbook": search_runbook,
"list_recent_deploys": list_recent_deploys,
}
# Optional: a simple tool schema you can feed into tool-calling LLMs
# If your provider uses a different schema, adapt it inside llm_chat().
TOOLS_SCHEMA = [
{
"name": "get_cpu_utilization",
"description": "Fetch CPU utilization stats for a service over the last 24h.",
"parameters": {
"type": "object",
"properties": {"service": {"type": "string"}},
"required": ["service"],
},
},
{
"name": "search_runbook",
"description": "Search internal runbooks for investigation and mitigation steps.",
"parameters": {
"type": "object",
"properties": {"query": {"type": "string"}},
"required": ["query"],
},
},
{
"name": "list_recent_deploys",
"description": "List recent deploys for a service to help correlate incidents.",
"parameters": {
"type": "object",
"properties": {"service": {"type": "string"}},
"required": ["service"],
},
},
]
# --------------------------------------
# 2) LLM hook (you implement this part)
# --------------------------------------
def llm_chat(messages: List[Dict[str, str]], tools_schema: List[Dict[str, Any]]) -> Dict[str, Any]:
"""
Implement this for your LLM provider.
Return one of these shapes:
A) Normal assistant message:
{"type": "message", "content": "..."}
B) Tool call request:
{"type": "tool_call", "tool_name": "search_runbook", "arguments": {"query": "high cpu"}}
Tip:
- If your LLM supports native tool calling, translate its tool-call response into shape (B).
- If your LLM does not support tool calling, you can prompt it to output JSON only.
"""
raise NotImplementedError("Implement llm_chat() for your provider.")
# ----------------------------
# 3) Minimal agent
# ----------------------------
@dataclass
class AgentConfig:
max_steps: int = 6
max_tool_calls: int = 4
timeout_seconds: int = 20
class SimpleAgent:
def __init__(self, config: AgentConfig):
self.config = config
def run(self, goal: str) -> str:
start = time.time()
tool_calls = 0
messages: List[Dict[str, str]] = [
{
"role": "system",
"content": (
"You are a careful DevOps assistant. "
"Only call tools when necessary. "
"Never invent metrics or links. "
"If information is missing, ask for it."
),
},
{"role": "user", "content": goal},
]
for step in range(self.config.max_steps):
if time.time() - start > self.config.timeout_seconds:
return "Stopped: timed out."
response = llm_chat(messages, TOOLS_SCHEMA)
# 1) If the LLM replies normally, we are done
if response.get("type") == "message":
content = (response.get("content") or "").strip()
return content if content else "Stopped: empty final message."
# 2) If the LLM wants to call a tool
if response.get("type") != "tool_call":
return "Stopped: unexpected response format from llm_chat()."
if tool_calls >= self.config.max_tool_calls:
return "Stopped: tool call limit reached."
tool_name = response.get("tool_name")
args = response.get("arguments", {})
tool_fn = TOOL_REGISTRY.get(tool_name)
if not tool_fn:
messages.append(
{"role": "assistant", "content": f"Tool not allowed: {tool_name}. Continue without it."}
)
continue
try:
tool_calls += 1
result = tool_fn(**args)
except Exception as e:
messages.append({"role": "assistant", "content": f"Tool '{tool_name}' failed: {e}"})
continue
# Feed tool result back to the LLM as plain text for simplicity
messages.append(
{
"role": "assistant",
"content": (
f"Tool result:\n"
f"tool_name={tool_name}\n"
f"args={json.dumps(args)}\n"
f"result={json.dumps(result, indent=2)}"
),
}
)
return "Stopped: max steps reached without a final answer."
# ----------------------------
# 4) Example usage
# ----------------------------
if __name__ == "__main__":
agent = SimpleAgent(AgentConfig())
outcome = agent.run(
"Investigate why payments-api CPU might be high and suggest next steps. "
"Use runbooks if relevant and correlate with recent deploys."
)
print(outcome)
No responses yet