No more guesswork with LLMs. This guide walks you through the small set of agentic patterns that actually work in practice — what they mean, when to pick them, and how they look in clear architecture diagrams.
Most LLM apps are like smart autocomplete — you ask, they reply. Agentic AI goes further — it:
plans steps,
uses tools/APIs,
checks its work, and
asks a human before doing anything risky.
In this blog, we’ll cover the patterns you’ll see in about 90% of production systems.
Rule of thumb: start simple. If one LLM call is enough, use it. Add more structure only when it boosts reliability, safety, or scale.
What is Agentic AI
Agentic AI is an LLM that can make small decisions, take actions, and learn as it goes. Think of it as a careful helper that works in steps:
Observe → Think → Plan → Act → Verify → Learn
Observe: read the request and any needed docs or logs.
Think: decide the next best move.
Plan (optional): outline a few steps if the task is bigger. Act: call a tool/API, run a query, or write a file.
Verify: check quality/safety; ask a human if it’s risky.
Learn: save useful notes so the next run is better.
Agentic AI Design Patterns ( Core 4 ) The four patterns you’ll use most — again and again.
- Tool Use (Function Calling) What it is?
Tool Use patternlets an agent go beyond what it “knows” by calling external tools — APIs, databases, or custom functions. With this pattern the agent can:
fetch real-time data,
run calculations, and
perform actions in other systems.
The key is that the agent decides when a tool is needed, picks the right tool, and then uses the tool’s result to finish the task.
When to use
Look up a record “What’s the order status for ID 4829?” → call get_order(id=4829) and return status/ETA.
Transform or convert “Turn this CSV into JSON.” → call convert_csv_to_json(file=...).
Light analytics “What’s the 95th percentile of these numbers?” → call stats.percentile(values, 95).
Trigger one action “Create a short URL for this link.” → call shorten_url(url=...).
Send a notification “Email the report to finance.” → call send_email(to=..., attachment=...) (often behind approval). Cons
If the request isn’t clear, the model may call a tool with the wrong fields. Use schema validation and show friendly error messages. Calls can time out or fail. Add timeouts, retries with backoff, and sensible error handling.
Some tools change real systems. Allow-list approved tools only and keep audit logs of every call.
Architecture Diagram
- Planning (Plan-then-Execute) What it is?
The agent creates a short plan first, then executes each step with a quick verify. If a step fails, it repairs or replans and continues. If you’ve seen “ReAct,” think of it as interleaving reasoning and actions. Planning is the more explicit, step-by-step version.
When to use
Data pipeline / report “Ingest → clean → join → validate → export dashboard.” Each stage runs only after the previous one passes a quick check.
Research brief “Find 5 sources → extract key facts → draft → fact-check → finalize with citations.”
Over-planning wastes tokens and time (keep plans to 3–8 steps).1
More steps can slow things down. Add time limits per step to control latency.
Needs checkpoints and idempotency to survive retries/crashes.
Architecture Diagram
- Reflection
What it is?
The agent gets better by reviewing its own work. It first creates an output, then — either the same model in a second step or a separate critic model — checks for mistakes and unclear parts, gives feedback, and the agent revises the answer. This loop boosts accuracy, reliability, and autonomy, so the system needs less constant human oversight.
When to use
Customer emails / chat replies Keep messages accurate, polite, and within policy; flag or remove risky phrases.
Code patches Ensure the change compiles, tests pass, and suggest a safer alternative if needed.
Regulated text Enforce approved wording, avoid medical/financial claims, and require citations.
Summaries Demand sources and relevance so as to cut down on hallucinations and fluff.
Cons
Adds cost and time. Limit to no more than 2 passes.
Vague checklists cause over-editing. Keep checks specific and concrete.
Avoid infinite fix loops. Set a clear stop condition (e.g., “stop after 2 tries or if no new issues found”).
Use several small, specialized AI agents that work together on one goal. The big task is split into subtasks and delegated to different agents (e.g., a planner, an executor, a reviewer). They communicate, coordinate, and share progress so the team delivers a single, better result than a lone agent. When to use
Parallel research “Agent A finds sources, Agent B extracts numbers, Agent C writes the draft; Supervisor merges and checks.”
Complex operations “Security triage: one agent scans logs, another checks indicators of compromise, a third proposes actions; Supervisor coordinates and escalates.”
Data quality ops “Worker A fixes schema, Worker B flags anomalies, Worker C writes the report; Supervisor tracks status to ‘done.’”
Cons
Coordination overhead: Multiple agents can trip over each other. Use locks/leases (simple “check-out” rules) to avoid race conditions and duplicate work.
Harder to observe/debug: Add traces, task IDs, and a shared state/blackboard so you can see who did what and why.
Cost and latency can climb: Start small — often two agents (doer + checker) are enough. Scale up only when you see a clear benefit.