A feed event can be a useful trigger for work: a new release note might create a review task, a corrected article might invalidate a summary, or a catalog update might prompt an internal check. The trigger should not, however, grant an agent new authority. A dependable workflow separates the fact that something arrived from the decision about what may happen next.
This guide proposes a bounded event-driven design for an assistant that reads items and suggests follow-up work. It does not assume that every step needs a model or that an agent should publish, send, or modify records without approval. Start with the Agent AI Feeds API overview and identify one useful action your team would otherwise perform manually. Then define the evidence and permission required for that action.
Decide whether an agent is necessary
A fixed sequence may be sufficient: receive an item, validate it, assign a category, and place it in a queue. Dynamic planning adds complexity when the path is already known. Use a model for the part that benefits from interpretation, rather than giving the entire workflow an open-ended objective by default.
Anthropic’s Building effective agents distinguishes workflows with predefined code paths from agents that dynamically direct their processes and tool use. It emphasizes starting with simple approaches and adding complexity when it improves the task. For a feed integration, that distinction is a useful design question: does the system need to choose its next step, or does it merely need to interpret an item within a known process?
Define a narrow event contract
An event should identify its source, the affected item, the relevant revision, the event type, and when your system observed it. Keep the event identity separate from the item identity. A single article may produce a new-item event and later a correction event. The workflow needs to distinguish those occurrences without losing their relationship.
Avoid making the raw source body the entire event contract. External text is evidence to inspect, not an instruction to execute. The orchestrator should determine the permitted task from trusted configuration. For example, a release-note event might authorize analysis and a proposed issue draft, but not permission to merge code, change a deployment, or contact an external recipient.
Separate interpretation from execution
Let the interpretive step produce a proposal in a constrained shape. A proposal could contain an action type, a target drawn from an allowed set, a short rationale, and references to the source evidence. Validate that structure before it reaches any executor. A model-generated tool name or arbitrary destination should not become an executable instruction merely because it appeared in a valid JSON object.
Keep the executor responsible for checking permissions and current state. It should verify that the requested operation is allowed for this workflow and this event. A proposal can be well formed yet unauthorized, outdated, or redundant. This separation makes it possible to review suggestions without exposing credentials or granting the model direct control over every system the application can reach.
Use explicit states and stop conditions
Represent the workflow with named states such as received, validated, analyzed, awaiting approval, completed, and failed. Define which transitions are allowed and what information each transition records. Avoid a loop whose only completion rule is the model deciding that it is finished.
Set limits on steps, runtime, input size, and repeated attempts. Decide what happens when a limit is reached: the workflow might stop with a review task rather than continuing to seek another tool. Keep the stopped state visible. A bounded failure with useful context is better than an apparently active run that can consume resources indefinitely without making a decision a person can understand.
Make actions safe to retry
Assume a worker can lose confirmation after an external system accepts an action. Retrying the operation may create a duplicate unless the integration can recognize the earlier attempt. Define an idempotency key or another deliberate deduplication mechanism for each action type, based on the event and intended effect rather than on a generated sentence.
For a proposed issue, the key might combine the workflow identity, source event identity, and action type. Before retrying, check whether that action has already been completed or recorded as pending. Keep request and result identifiers where the external system provides them. Do not equate a deduplicated local event with guaranteed exactly-once behavior across every downstream service; inspect the actual failure boundaries.
Put approval at the consequential boundary
Decide which actions can occur automatically and which require a person’s approval. Reading a public item and preparing an internal suggestion may be low consequence. Publishing content, sending a message, spending money, or changing a production system can require a more explicit review step. The approval should refer to a concrete proposed action, not a vague goal that can expand later.
Show the reviewer the source, proposed effect, target, and relevant uncertainty. If the proposal changes after approval, require a new decision when the change is material. Do not let an approval for one recipient or record silently authorize a different one. Keep the review interface focused on what will happen, because a fluent rationale can distract from an incorrect target or an unexpectedly broad operation.
Keep untrusted content away from permissions
A feed item may contain instructions directed at an assistant. Treat those instructions as part of the item’s content, not as application policy. The workflow should derive its tools, credentials, and allowed destinations from trusted configuration outside the document. Source text should not be able to promote itself into a new role or grant access to private collections.
Reduce the available capabilities to those required for the specific task. A classification worker does not need a general shell or unrestricted network access. A drafting worker does not need permission to send its draft. These boundaries do not depend on the model perfectly recognizing malicious text. They constrain the application’s possible effects even when an interpretation step behaves unexpectedly.
Record an audit trail that explains the result
Keep the event identity, source revision, task version, proposal, validation outcome, approval record where applicable, and execution result. The purpose is to explain what happened without requiring someone to reconstruct it from scattered messages. Avoid logging secrets or unnecessary copies of sensitive source material simply because a verbose trace is easy to capture.
A readable run summary should distinguish “the model suggested this,” “the policy allowed this,” and “the executor completed this.” Those are separate claims. If an action was skipped because it already existed, say so rather than reporting a fresh completion. When a run fails, preserve the stage and reason so an operator can decide whether retry, correction, or manual handling is appropriate.
Test adversarial and ordinary interruptions
Build fixtures for a duplicate event, a corrected source, an unsupported action proposal, a missing target, and an item containing instructions that conflict with the task. Also test mundane failures: a slow tool, a temporary external error, and a restart after an action was accepted but before the result was recorded.
Write the expected outcome for each case. A duplicate should not produce a second effect. A changed source may require renewed analysis. An unauthorized proposal should stop before execution. Use the server feed reliability guide to plan the transport and checkpoint behavior beneath the agent layer. Adding an agent does not remove the need for dependable ordinary software boundaries.
Expand only after the small workflow is clear
Start with one event type, one proposed action, and a visible review path. Measure useful completions, review effort, incorrect proposals, and stopped runs under your own conditions. Add autonomy only when it solves a demonstrated problem without making the permission boundary harder to inspect.
A good agent feeds workflow does not turn every incoming item into an opportunity for unconstrained action. It turns a specific kind of event into a bounded, evidence-linked process. Keep triggers separate from authority, interpretation separate from execution, and approval tied to the actual effect. Those distinctions make automation useful while leaving people and application policy in control of consequential decisions.
