Trade-offs: LLM Centric Planning vs Backend Orchestration

The Central Design Decision: When building agent systems, you must decide where intelligence lives: in the LLM's dynamic planning or in your backend's deterministic orchestration. This choice fundamentally shapes system behavior, reliability, and cost. LLM Centric Planning: In this approach, the LLM has full autonomy. It receives a list of available tools and decides which to call, in what sequence, and when it has enough information to answer. Patterns like ReAct (reason and act) or planning and execution frameworks give the model maximum flexibility. The advantage is adaptability. The LLM can discover novel tool combinations you never explicitly programmed. If a user asks an unexpected question, the agent might chain together three tools in a creative way that solves the problem. You write less custom code and can add new tools without rewriting orchestration logic. The cost is unpredictability. The same query might take 2 tool calls one day and 5 calls the next, making latency Service Level Agreements (SLAs) harder to guarantee. Debugging failures is difficult because you cannot easily reproduce the exact decision path the LLM took. Testing requires running the full agent against diverse scenarios rather than unit testing specific code paths. At scale, this variability matters. If 5 percent of requests unexpectedly take 8 tool steps instead of 2, your p95 latency blows up. Your cost also becomes less predictable: some days you average 3 LLM calls per request, other days 5, and your bill varies by 40 percent. Backend Orchestration: Here, your backend code contains explicit routing rules. For example: if the query is about user data, call get_user_profile, then call the LLM to format the response. If it's about orders, call search_orders, then get_order_details, then synthesize. The LLM is only used to extract parameters from natural language or generate final text. This maximizes predictability. Every query follows a known path. You can measure exact latency per flow, set SLAs per use case, and unit test each branch. Your cost is deterministic: each order query costs exactly 2 LLM calls plus 2 tool calls. The tradeoff is rigidity. You must anticipate every user intent and hard code the tool sequence. Adding new capabilities requires engineering work. The system cannot adapt to edge cases you didn't foresee. If a user asks something slightly outside your defined flows, the agent fails rather than improvising. When to Choose Each: Choose LLM centric planning when: your domain is open ended and you need adaptability. Research assistants, creative tools, and exploratory agents benefit from flexible planning. You have tolerance for variable latency and cost, perhaps because requests are asynchronous or users expect 3 to 5 second response times. You have strong offline evaluation to catch bad behaviors before production. Choose backend orchestration when: you need strict SLAs, for example customer facing APIs with p95 under 1 second. You operate in regulated domains where auditability and determinism matter. Your use cases are well defined, like order lookup or account management. You want to minimize cost by avoiding unnecessary LLM calls. The Hybrid Reality: Most production systems use a hybrid. The backend orchestrates high level flows (authentication, intent classification, tool selection constraints), while the LLM plans within bounded sub tasks. For example: the backend decides "this is an order question" and provides only order related tools. Within that scope, the LLM can flexibly decide whether to search by order ID, by date range, or by product, and whether to fetch details or just summaries. This gives you 70 percent of the flexibility with 80 percent of the predictability.