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What happens inside an agent’s work loop?
A useful way to understand an agent is plan → act → observe → update → verify. The plan may be a short next step or a longer sequence; either way, new information can change what the agent should do next.
- Interpret the goal. The system identifies the requested outcome and any constraints, such as what information must be gathered or what actions are permitted.
- Choose the next step. The model or workflow decides whether it can proceed from what it already knows, needs a tool, needs clarification, or should stop.
- Act through an available interface. A tool call might retrieve information or change an external environment. The agent can only use capabilities exposed to it by the surrounding software.
- Inspect the observation. The runtime receives a result, error, or other feedback. It must distinguish that observation from assumptions made by the model.
- Update and verify. The plan can be revised in light of the observation. Before declaring completion, the system should check that the result satisfies the task rather than merely that a call returned.
This interleaving of reasoning and action is central to ReAct, a framework in which reasoning traces and task-specific actions alternate. The authors describe reasoning traces as helping a model “induce, track, and update action plans as well as handle exceptions,” while actions gather information or interact with an environment. ReAct’s experiments reported absolute success-rate improvements of 34% on ALFWorld and 10% on WebShop over the imitation and reinforcement-learning methods compared in the paper’s few-shot benchmark setup; those task-specific results are not estimates of general real-world reliability. ReAct paper (ICLR 2023)
How does an agent decide whether and how to use a tool?
Tool use involves more than selecting a function name. The system has to determine whether a tool is needed, choose an appropriate API, form valid arguments, and incorporate the returned result into its next step. Toolformer presents one training approach: it trains language models to make these decisions and use returned API results in subsequent generation. That is one approach, not a requirement; other agents rely on prompting, orchestration code, or a combination. Toolformer (2023)
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In practice, the model’s proposed action and the runtime’s responsibilities are distinct. The model can suggest a call, but the surrounding system exposes the tool, checks its schema and permissions, executes it, records what happened, and returns feedback. A well-formed call can still be unsuitable for the goal, and a valid tool response can still be misread or semantically wrong.
- Availability: Is there a tool that can perform the needed action, and is it enabled for this task?
- Arguments: Do the supplied values meet the API’s expected schema and constraints?
- Permissions and side effects: Is the requested operation allowed, and could it make an external change that needs confirmation or tighter limits?
- Result handling: What does the returned information establish, and what remains uncertain?
What kinds of errors can occur?
“The tool failed” is too broad to guide a repair. A failure may happen before a call, during execution, or after a response has arrived. Microsoft Research’s AgentRx work organizes failures in agent trajectories across such causes as skipped or extra actions, invented facts, malformed calls, mistaken readings of tool outputs, plans based on misunderstood intent, missing information, unsupported tools, safety or access blocks, and connectivity or endpoint problems. Microsoft Research, AgentRx (March 12, 2026)
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- Goal or planning error: The agent misunderstands the request, omits a necessary step, or takes an unnecessary action.
- Call-construction error: The tool exists, but the call is malformed or its arguments do not match the intended operation.
- Capability or access failure: The needed tool is unavailable, or a safety, permission, or access control prevents its use.
- Execution or connection failure: A service, endpoint, or network problem prevents the expected response.
- Interpretation or state error: The response arrives, but the agent draws the wrong conclusion, loses track of what has already happened, or treats an assumption as an observation.
- Semantic failure: The response looks plausible and arrives without a technical error, yet does not correctly answer the question or perform the intended action.
Silent semantic failures are particularly difficult because ordinary error signals may not expose them. The ToolMaze study examines replanning when tools are perturbed and reports that implicit semantic failures can sharply affect recovery. Its benchmark findings concern the studied perturbations and tasks, not a universal failure rate for deployed agents. ToolMaze (June 4, 2026)
How should an agent recover when something goes wrong?
A useful recovery policy diagnoses the likely cause before choosing a response. Repeating the same call without changing anything may reproduce the same mistake; recovery should address the suspected failure and then check whether the correction worked. The following sequence is a practical synthesis of failure-analysis work, not a guarantee that every agent implements each step.
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- Detect the discrepancy. Look for an invalid output, an unavailable tool, a mismatch between the returned result and the request, or a task condition that remains unmet.
- Localize the failing step. Check whether the problem arose from call construction, tool availability, result interpretation, state tracking, or understanding the goal.
- Choose a cause-matched response. Repair arguments, request missing information, select a supported alternative, revisit an earlier step, replan, or stop and escalate when continuing would be unsafe or unsupported.
- Verify the revised result. Compare it with the task’s actual success condition, using an independent check where appropriate, before proceeding or reporting completion.
Microsoft Research reports that AgentRx evaluated 115 manually annotated failed trajectories across τ-bench, Flash, and Magentic-One. In that framework and against prompting baselines, the report gives improvements of 23.6% in failure localization and 22.9% in root-cause attribution. These are results for that benchmark and comparison, not evidence that all agent systems recover more reliably by the same margins. AgentRx overview
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can you compare agent designs?
When assessing two implementations, look beyond whether each completed a task. The design choices below affect how the system acts, diagnoses errors, and demonstrates that it has reached the requested outcome. These are comparison dimensions synthesized from ReAct, Toolformer, AgentRx, and ToolMaze—not a universal published standard.
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| Dimension | What to examine | Why it matters |
|---|---|---|
| Plan structure | Does the agent revise steps as it goes, maintain an explicit plan, or follow a workflow with fixed stages? | Shows whether new observations can change the course of action. |
| Tool interface | Which tools are exposed? Are argument schemas, permissions, and validation explicit? What feedback follows an invalid request? | Clarifies what actions the model can actually take and how call errors are surfaced. |
| State and observations | How are completed actions recorded, and can the system distinguish tool output from model assumptions? | Helps prevent duplicated actions and conclusions based on unverified assumptions. |
| Failure diagnosis | Can the system identify which step failed and whether the cause was intent, arguments, tool support, execution, or interpretation? | A diagnosis makes a targeted correction possible instead of an unchanged retry. |
| Recovery policy | Can it repair a call, try a supported alternative, backtrack, replan, request clarification, or hand off? Are attempts and side effects limited? | Defines how the agent behaves when its first plan does not work. |
| Verification and evaluation | Does it check task conditions, and are evaluations limited to task success or also measure diagnosis and recovery under controlled failures? | A successful-looking response alone does not establish that the result is correct. |
Benchmark findings should be read within their stated settings: the task suite, comparison method, and failure conditions shape what a result supports. Results from ALFWorld, WebShop, annotated failed trajectories, or controlled tool perturbations do not establish an industry-wide reliability figure.
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