Drop your ASIN - get an optimized listing and push it to Amazon Seller Central in one click. Drop your ASIN - get an optimized listing, push to Amazon. Try it →AI agents work by running a loop. Perceive the situation. Plan a next action. Take that action. Observe what happened. Decide whether to keep going or stop. Repeat. That's the entire mechanism, dressed up with different names by different vendors. This page walks through the loop with a real ecommerce example and shows where it breaks.
An AI agent runs a think-act-observe loop. A model (usually an LLM) decides what to do next. A tool executes that decision. The result feeds back into the next decision. The loop runs until the agent reaches its goal, hits a step limit, or gives up. Everything else is plumbing.
Every production AI agent in 2026 runs some version of this loop. The names vary (ReAct, plan-and-execute, function-calling loop), but the structure is the same.
That loop is the entire mechanism. The cleverness lives in step 2 (a smarter model plans better) and step 3 (better tools enable more capability). Steps 1, 4, and 5 are bookkeeping the framework handles.
Let me walk through one full agent run. Take an Amazon listing-optimization agent on the SellerShorts marketplace. Seller drops in an ASIN. What happens.
sellershorts
sp_api_get_catalog_item.get_competitor_listings.get_search_term_report.check_amazon_style_compliance.Five iterations. Four tool calls. One round of pure reasoning. Total cost: maybe $2 in model tokens plus tool fees. Total time: 30 to 90 seconds. That's an agent doing real work.
I've used "tool" several times. Worth being precise. In agent terminology, a tool is anything the model can call to act on or learn from the outside world. The model itself only generates text. Tools are how the agent gets information that isn't in the model's training and how it performs actions in the real world.
Common tool types in ecommerce agents:
The Model Context Protocol (MCP) is Anthropic's standard for how agents declare and call tools. Amazon shipped the Amazon Ads MCP Server in open beta on February 2, 2026, which lets any MCP-compatible agent talk to Amazon Ads using the standard. Deeper coverage in tools and capabilities.
This is the magic step that surprises people. The model gets a description of every tool it has access to. The descriptions are part of the system prompt the agent's vendor wrote. When the model is planning, it looks at the description, decides which tool fits the situation, and emits a structured tool call (usually JSON) that the framework intercepts and executes.
Tool descriptions matter a lot. Vague descriptions produce wrong tool choices. Strong tool descriptions include the tool name, what it does, what parameters it takes, and what it returns. The quality of the tool descriptions is a huge factor in agent reliability that almost nobody talks about.
An agent has to know when to stop. Three common exit conditions in production.
Agents that lack step limits are a common cost-runaway disaster. A bug or an unsolvable goal can drive a single agent run into the hundreds of model calls. Always cap the loop.
Three failure modes I see often, plus what good agents do about them.
SP-API times out. The agent tries again, hits the rate limit. Tries a fallback tool, gets a different error. By iteration 8 the state is full of error messages and the model gets confused.
Fix: graceful tool failure handling. Retries with backoff. Clear error context. The model should know "this tool failed three times, try a different approach" instead of getting an opaque error and panicking.
The agent starts optimizing for the wrong thing. It was supposed to improve conversion, but somewhere around iteration 7 it's now stuffing keywords because that was the most recent step's optimization signal.
Fix: re-state the goal in every iteration. The system prompt structure should keep the original goal at the top of context. Anthropic's Building Effective Agents covers this pattern.
Long runs accumulate state. Each tool call adds output to context. By iteration 15, the context window is full and old information gets dropped silently. The agent forgets what it was doing.
Fix: summarization between iterations. Selective retrieval (only pull in the relevant prior steps). Or just don't let the loop get that long, which is the simpler answer.
Most production agents run as serverless functions or in dedicated agent runtimes. The seller doesn't see this. They see a "Run" button and a result. Under the hood, the framework:
This is the layer marketplaces like SellerShorts hide for you. The framework, the model API key, the tool integrations, the loop management. You bring the input, get the output.
Two flavors of the agent loop you'll see referenced.
The model alternates between reasoning ("I should fetch the listing") and acting ("call SP-API"). Each step is short. The loop adapts as new information arrives. Most production agents in 2025-2026 use this style.
The model writes a complete plan upfront ("step 1, step 2, step 3, step 4"). Then executes each step in sequence. Useful when the plan can be reasoned about up front and doesn't need to adapt. Less common in dynamic ecommerce ops.
Most real agents are a hybrid. They plan a bit, react to results, replan if needed. Strict adherence to one pattern is mostly academic.
| Pattern | When to use it | Strengths | Weaknesses |
|---|---|---|---|
| ReAct (Reason + Act) | Dynamic tasks where each step depends on the previous result (listing optimization, PPC analysis, customer triage). | Adapts to surprises. Cheap to start. Easy to debug step by step. | Can loop forever. Sometimes wanders off-goal without a strong system prompt. |
| Plan-and-Execute | Predictable, multi-step jobs where the plan is knowable upfront (bulk catalog enrichment, scheduled report generation). | Lower token cost. Parallelizable steps. Easier to audit before running. | Brittle when reality diverges from the plan. Replanning costs more than ReAct iterations. |
| Hybrid | Real production agents in 2026. Most ecommerce workflows fit here. | Balances adaptivity with structure. Plans the spine, reacts on the edges. | Harder to test. More configuration to get right. Most frameworks default to this anyway. |
If you're a seller using agents (not building them), three things you can safely ignore.
What you do need to understand: the loop exists, what tools the agent has access to, where it stops, what happens when it fails. Those four things let you evaluate any agent on the market.
Anthropic released Computer Use in late 2024, and OpenAI shipped Operator in 2025. Both expand the agent loop to include "use the actual user interface a human would use." Instead of calling an API, the agent moves a cursor, clicks buttons, types into forms.
This is interesting because it dramatically expands what agents can do (anything a human can do on a screen) but adds new failure modes (UI changes break the agent, screenshots eat tokens, the loop is slow). For Amazon sellers specifically, Computer Use is overkill for most tasks. Direct API calls via SP-API and the Amazon Ads MCP Server are faster and more reliable for anything Amazon already exposes. Computer Use becomes relevant when you need agents to use systems that don't have APIs.
Treat Computer Use as a 2027-2028 production pattern for ecommerce. In 2026 it's a demo, not a default.
An AI agent runs a 5-step loop: perceive the current state, plan the next action, take that action, observe what happened, decide whether to keep going or stop. The loop repeats until the goal is reached or a limit is hit. Every production AI agent runs some version of this loop.
The agent gets a description of every tool it has access to. The model looks at the description, decides which tool fits the situation, and emits a structured tool call (usually JSON) that the framework executes. Tool description quality dramatically affects reliability.
Three common failures: tool failure cascades (one API timeout breaks the chain), goal drift (the agent starts optimizing for the wrong thing), and context overflow (long runs lose track of the original goal). Good agents have retry logic, goal re-statement, and context summarization.
ReAct alternates reasoning and acting one step at a time. Plan-and-execute writes a full plan upfront then executes step by step. Most production agents in 2026 use a hybrid that plans a bit, reacts to results, and replans as needed.
SellerShorts hosts the agent loop for you. The framework, the model, the tools, the retries. You drop an input, the agent runs, you get a result. No infrastructure.
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