{"slug": "agentic-ai-design-patterns-that-90-of-teams-use", "title": "Agentic AI Design Patterns that 90% of Teams Use", "summary": "A new guide from an unnamed source outlines four core agentic AI design patterns—Tool Use, Planning, Reflection, and a fourth pattern—that appear in about 90% of production systems, emphasizing a start-simple approach and adding structure only when it improves reliability, safety, or scale. The patterns enable LLMs to plan steps, use tools/APIs, verify work, and ask humans before risky actions, with specific use cases like fetching order status, converting CSV to JSON, and drafting research briefs.", "body_md": "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.\n\nMost LLM apps are like smart autocomplete — you ask, they reply. Agentic AI goes further — it:\n\nplans steps,\n\nuses tools/APIs,\n\nchecks its work, and\n\nasks a human before doing anything risky.\n\nIn this blog, we’ll cover the patterns you’ll see in about 90% of production systems.\n\nRule of thumb: start simple. If one LLM call is enough, use it. Add more structure only when it boosts reliability, safety, or scale.\n\nWhat is Agentic AI\n\nAgentic 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:\n\nObserve → Think → Plan → Act → Verify → Learn\n\nObserve: read the request and any needed docs or logs.\n\nThink: decide the next best move.\n\nPlan (optional): outline a few steps if the task is bigger.\n\nAct: call a tool/API, run a query, or write a file.\n\nVerify: check quality/safety; ask a human if it’s risky.\n\nLearn: save useful notes so the next run is better.\n\nAgentic AI Design Patterns ( Core 4 )\n\nThe four patterns you’ll use most — again and again.\n\n1) Tool Use (Function Calling)\n\nWhat it is?\n\nTool Use patternlets an agent go beyond what it “knows” by calling external tools — APIs, databases, or custom functions. With this pattern the agent can:\n\nfetch real-time data,\n\nrun calculations, and\n\nperform actions in other systems.\n\nThe 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.\n\nWhen to use\n\nLook up a record “What’s the order status for ID 4829?” → call get_order(id=4829) and return status/ETA.\n\nTransform or convert “Turn this CSV into JSON.” → call convert_csv_to_json(file=...).\n\nLight analytics “What’s the 95th percentile of these numbers?” → call stats.percentile(values, 95).\n\nTrigger one action “Create a short URL for this link.” → call shorten_url(url=...).\n\nSend a notification “Email the report to finance.” → call send_email(to=..., attachment=...) (often behind approval).\n\nCons\n\nIf the request isn’t clear, the model may call a tool with the wrong fields. Use schema validation and show friendly error messages.\n\nCalls can time out or fail. Add timeouts, retries with backoff, and sensible error handling.\n\nSome tools change real systems. Allow-list approved tools only and keep audit logs of every call.\n\nArchitecture Diagram\n\n2) Planning (Plan-then-Execute)\n\nWhat it is?\n\nThe 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.\n\nWhen to use\n\nData pipeline / report “Ingest → clean → join → validate → export dashboard.” Each stage runs only after the previous one passes a quick check.\n\nResearch brief “Find 5 sources → extract key facts → draft → fact-check → finalize with citations.”\n\nOver-planning wastes tokens and time (keep plans to 3–8 steps).1\n\nMore steps can slow things down. Add time limits per step to control latency.\n\nNeeds checkpoints and idempotency to survive retries/crashes.\n\nArchitecture Diagram\n\n3) Reflection\n\nWhat it is?\n\nThe 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.\n\nWhen to use\n\nCustomer emails / chat replies Keep messages accurate, polite, and within policy; flag or remove risky phrases.\n\nCode patches Ensure the change compiles, tests pass, and suggest a safer alternative if needed.\n\nRegulated text Enforce approved wording, avoid medical/financial claims, and require citations.\n\nSummaries Demand sources and relevance so as to cut down on hallucinations and fluff.\n\nCons\n\nAdds cost and time. Limit to no more than 2 passes.\n\nVague checklists cause over-editing. Keep checks specific and concrete.\n\nAvoid infinite fix loops. Set a clear stop condition (e.g., “stop after 2 tries or if no new issues found”).\n\nUse 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.\n\nWhen to use\n\nParallel research “Agent A finds sources, Agent B extracts numbers, Agent C writes the draft; Supervisor merges and checks.”\n\nComplex operations “Security triage: one agent scans logs, another checks indicators of compromise, a third proposes actions; Supervisor coordinates and escalates.”\n\nData quality ops “Worker A fixes schema, Worker B flags anomalies, Worker C writes the report; Supervisor tracks status to ‘done.’”\n\nCons\n\nCoordination overhead: Multiple agents can trip over each other. Use locks/leases (simple “check-out” rules) to avoid race conditions and duplicate work.\n\nHarder to observe/debug: Add traces, task IDs, and a shared state/blackboard so you can see who did what and why.\n\nCost and latency can climb: Start small — often two agents (doer + checker) are enough. Scale up only when you see a clear benefit.", "url": "https://wpnews.pro/news/agentic-ai-design-patterns-that-90-of-teams-use", "canonical_source": "https://pub.towardsai.net/agentic-ai-design-patterns-that-90-of-teams-use-03b3bb481d62?source=rss----98111c9905da---4", "published_at": "2026-08-04 13:36:32+00:00", "updated_at": "2026-08-04 14:22:55.106647+00:00", "lang": "en", "topics": ["artificial-intelligence", "large-language-models", "ai-agents", "ai-tools", "ai-research"], "entities": [], "alternates": {"html": "https://wpnews.pro/news/agentic-ai-design-patterns-that-90-of-teams-use", "markdown": "https://wpnews.pro/news/agentic-ai-design-patterns-that-90-of-teams-use.md", "text": "https://wpnews.pro/news/agentic-ai-design-patterns-that-90-of-teams-use.txt", "jsonld": "https://wpnews.pro/news/agentic-ai-design-patterns-that-90-of-teams-use.jsonld"}}