Agent orchestration: code-based flow vs LLM planning

code-based control beats llm-driven planning when your workflow needs predictable speed, cost and performance. the choice is structural: hardcode the decision path, or let the model plan autonomously. hcltech's multi-agent setup cut case resolution time by 40% — that's the difference between deterministic flow and probabilistic guesswork.

the two camps

Feature	LLM Orchestration	Code Orchestration
Control Owner	Model (Runtime)	Developer (Pre-set)
Predictability	Low (Probabilistic)	High (Deterministic)
Best Use Case	Open-ended exploration	Fixed workflows

llm planning only wins on open-ended scenarios needing flexible tool use. code state machines eliminate the variability — if you need predictability, you need code guards. and no single prompt does both reliably.

agents as tools vs handoffs

Pattern	Control Ownership	Best Use Case
Agents as tools	Manager Agent	Bounded subtasks; unified output synthesis
Handoffs	Specialist Agent	Direct response requirements; focused prompts

rule of thumb: if the manager must narrate results, use tools. if the specialist must drive the dialogue, use handoffs. and you can combine them — triage agent hands off, the specialist then calls other agents as tools for narrow computations.

the human bottleneck

Risk Factor	Mitigation Strategy
Review Bottleneck	Limit active agents to 10-20 per function
Opaque Failures	Enforce deterministic code paths for critical steps
High Labor Cost	Optimize oversight workflows before adding agents

human review capacity, not agent count, binds production scaling. labor costs for supervision exceed compute expenses in human-in-the-loop setups. align agent count with actual review bandwidth before adding more.

reliability mechanics

Pattern	Mechanism	Constraint
Parallel Execution	asyncio.gather runs agents concurrently	Token budget multiplies by agent count
Evaluation Loop	while loop repeats until criteria met	Risk of infinite cycles without exit

parallel runs cut latency but burn tokens — cap the budget. evaluator loops force convergence but cost two extra model calls per failed pass. monitor rejection rates to tune evaluator strictness.

structured outputs fix routing

Failure Mode	Unstructured Output	Structured Output
Routing Logic	Substring matching fails on variations	Exact enum matching guarantees accuracy
Error Handling	Complex regex required	Native parsing exceptions
Context Usage	High token consumption for explanations	Minimal token overhead

force agents to emit json schemas instead of free text, parse the result in the app layer, and routing becomes code-verified instead of model-guessed. the cost: exhaustive category definitions upfront, schema validation failures to handle gracefully.

https://aiagentsnews.top/posts/agent-orchestration-code-based-flow-vs-llm-planning/