Agentic Workflows vs Agentic Chains
Chains: fixed, predetermined sequences of steps
A chain is a static pipeline of LLM calls and tools wired together in a fixed order, decided entirely by the developer in advance: retrieve → summarize → translate, or extract → validate → format. The control flow doesn't change based on what the model produces at each step — step 2 always runs after step 1, regardless of content. Chains are predictable, easy to test, and easy to debug, because the execution graph is known before you ever run the program.
Agentic workflows: the model decides what happens next
An agentic workflow gives the model itself control over what step happens next, usually through a loop: the model observes the current state, decides on an action (call a tool, ask a clarifying question, produce a final answer), executes it, observes the result, and decides again. The control flow is no longer fixed in code — it emerges from the model's own reasoning at runtime. This unlocks flexibility (the same agent can handle wildly different requests without new code paths) at the cost of predictability (you can't always know in advance what sequence of tool calls will run, or how many iterations it will take).
Chains vs agentic workflows
| Property | Chain | Agentic workflow |
|---|---|---|
| Control flow | Fixed, defined by developer | Dynamic, decided by the model at runtime |
| Predictability | High — same path every run | Lower — path varies with model decisions |
| Debuggability | Easy — deterministic trace | Harder — requires tracing/observability tooling |
| Cost/latency ceiling | Bounded, known in advance | Variable, can loop longer than expected |
| Best fit | Well-defined, repeatable tasks | Open-ended tasks with unknown step counts |
The spectrum in between: workflows with bounded autonomy
Most production systems don't pick a pure extreme — they sit somewhere on a spectrum. A common pattern is a router: a chain-like fixed first step classifies the request, then dispatches to one of several sub-chains or sub-agents. Another common pattern is bounded agentic loops: give the model autonomy to decide tool calls, but cap the number of iterations, restrict which tools it can access at each stage, and fall back to a deterministic path if the loop doesn't converge. This hybrid approach captures much of agentic flexibility while keeping worst-case cost and behavior bounded — a critical property for anything shipped to real users.
The real tradeoff is reliability engineering, not intelligence
# Chain: fixed order, no branching decided by the model
def run_chain(user_query, llm, retriever):
docs = retriever.search(user_query) # step 1, always runs
summary = llm.call(f"Summarize: {docs}") # step 2, always runs
answer = llm.call(f"Answer using: {summary}") # step 3, always runs
return answer
# Agentic loop: the model decides the next action each iteration
def run_agent(user_query, llm, tools, max_steps=6):
state = {"query": user_query, "history": []}
for _ in range(max_steps):
action = llm.decide_next_action(state, available_tools=tools)
if action.type == "final_answer":
return action.content
result = tools[action.tool_name](**action.args)
state["history"].append((action, result))
return "max steps reached without a final answer"What's Next?
Whichever structure is chosen, the model needs a concrete, well-specified way to actually invoke external tools and get results back into its context — that interface is what makes either chains or agentic loops possible in the first place.
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