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Build the service as a normal incident-management API with an AI assistant inside its workflow—not as an autonomous responder with unrestricted access. FastAPI should handle authenticated requests and authorization; durable storage should hold incidents, event history, memory and job state; a narrowly permitted agent can analyze evidence and recommend next steps. Keep containment and recovery actions behind deterministic policy checks and, when their impact warrants it, human approval.
How do I build an incident response agent with FastAPI?
Separate the system into components with distinct responsibilities. This makes it easier to review what the model can see and do, preserve a reliable incident record, and change the model or storage choices without changing the API contract.
- HTTP API: Accept incident requests and return carefully defined incident, summary and job-status responses.
- Authorization and dependencies: Authenticate the caller, check their access to the specific incident, and inject shared services such as a database session or domain service into route handlers.
- Incident workflow: Coordinate analysis, recommendations, approvals and permitted actions. Keep business rules outside route handlers and outside model-generated text.
- Persistence: Store the incident, its event history, relevant memory, provenance for evidence and recommendations, and asynchronous job state in durable storage.
- Agent and tools: Give the model only the data and tools needed for the task. Treat its output as a proposal that must pass application policy before it can cause a consequential change.
- Worker: Run long or retryable investigations separately from the web request process.
FastAPI dependencies are a useful way to provide a principal, session and domain services consistently. They do not enforce the correct access policy automatically: each operation still needs an explicit authorization check. The FastAPI Dependencies documentation describes dependency injection, while the OWASP FastAPI Security Cheat Sheet emphasizes authorization as a separate control.
Keep request and response models narrow
Define typed models for operations such as creating an incident, appending an event, requesting an analysis and checking a job. Return only fields the caller is allowed to see; do not serialize internal notes, credentials, raw prompts, tool configuration or another tenant’s data just because those fields are present in an ORM object.
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For every read, write, summary, memory lookup and job-status request, check the caller’s role and the incident’s tenant or ownership boundary. Validate input shape, but do not mistake schema validation for authorization or SQL-injection protection. Use parameterized database operations and avoid returning raw validation exceptions or logging complete request bodies that may contain sensitive evidence.
Make the workflow auditable
Represent important transitions as domain events—for example, evidence added, analysis requested, recommendation recorded, approval granted or action completed. Record who or what initiated each transition, its time, the evidence or memory references used, and the outcome. Avoid putting secrets or unnecessary personal data into audit records.
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How should persistent agent memory work?
Persistent memory is durable, scoped application data—not a Python global and not an assumption that the model remembers prior requests. Save useful incident context in storage that survives process restarts and can be read by the right worker. FastAPI’s Deployment Concepts documentation notes that ordinary worker processes do not share application memory; a process-local cache can therefore be an optimization, not the source of truth.
Keep at least these records conceptually distinct:
- Incident state: Current status, ownership, severity and other operational fields.
- Event history: An appendable account of observations, decisions, approvals and actions.
- Memory entries: Curated facts or summaries that may help a later analysis, with a scope such as one incident, one user or one tenant.
- Provenance: References to the source event, document or observation behind a stored fact, plus timestamps or other context needed to assess its freshness.
- Job state: Status and result references for work performed asynchronously.
Do not merge these into a single ever-growing conversation transcript. A compact memory entry should be retrievable with its scope and provenance, and should not silently become authoritative merely because it was stored earlier. Define retention, correction and deletion behavior for each record type. The right database, search component, encryption configuration and retention period depend on the system’s data classification, scale and deployment requirements; there is no universally established choice here.
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A practical request path
- Authenticate: Establish the caller’s identity before processing the incident operation.
- Authorize and load: Verify access to the incident and load its current state and relevant, properly scoped records.
- Prepare evidence: Select only the logs, alerts and memory entries needed for this analysis, preserving references to their sources.
- Request analysis: Send the agent the task, relevant context and limited tool set. Clearly distinguish system instructions from submitted or retrieved content.
- Validate the proposal: Parse the result into an expected structure, check it against application policy and record its provenance. Validation of the result’s shape does not establish that its recommendation is safe or authorized.
- Persist and respond: Save the analysis and job or incident update, then return a response model that excludes internal fields.
- Gate any action: For consequential containment or recovery, require the configured authorization and approval path before executing a deterministic operation.
Should I use FastAPI BackgroundTasks or Celery?
Choose based on what the work must survive and how it runs, not merely on whether it happens after an HTTP response. FastAPI’s Background Tasks documentation says tasks can run after returning a response; it gives notifications and processing as examples, and notes that heavier computation that need not share the application process may benefit from a larger task system such as Celery.
| Choice | Good fit | Important limitation | Operational trade-off |
|---|---|---|---|
FastAPI BackgroundTasks |
Small post-response work, such as sending a notification or a brief follow-up operation. | It runs with the application process; do not rely on it as durable, retryable job execution that must survive process failure. | Simple to add, but tied to the web service’s process lifecycle and resources. |
| Separate worker and task queue, such as Celery | Long-running, heavier or retryable investigations that should proceed independently of the HTTP request process. | Requires explicit job-state persistence and operational handling for queues, retries, failures and worker lifecycle. | More components to operate, with better process separation for appropriate workloads. |
For an investigation job, persist a job identifier and status, return that identifier to the caller, and expose an authorized status endpoint. The worker should load the incident and scoped evidence from durable storage rather than depend on request-process memory. Decide explicitly how duplicate delivery, retries, cancellation, timeouts and partial failure affect the incident record.
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How do I prevent prompt injection in an incident response agent?
Assume uploaded logs, alerts, tickets, retrieved documents and memory text may contain malicious instructions. Treat them as untrusted evidence, not as instructions that can override the agent’s task or grant additional permissions. OWASP’s AI Agent Security Cheat Sheet identifies prompt injection and data exfiltration risks, and recommends least-privilege tools and screening memory for sensitive data before persistence.
- Separate instructions from evidence: Label external content as data, delimit it clearly and tell the model not to follow instructions embedded in that content. This helps communicate the boundary but is not a security control by itself.
- Constrain tools: Expose only task-relevant operations. Prefer read-only investigation tools by default; do not provide arbitrary shell, unrestricted network or general-purpose administrative access.
- Enforce permissions in code: Every tool call should independently verify identity, incident scope and policy. Never let the model decide that a caller is authorized.
- Gate high-impact actions: Require a policy decision and human approval where the action’s impact, reversibility or uncertainty calls for it. Execute through deterministic application code, not a free-form model command.
- Screen stored memory: Exclude or redact credentials and unnecessary sensitive data before persistence. Retain source references so reviewers can distinguish an observed fact from a model-generated summary.
- Limit disclosure: Restrict which incident fields the agent can retrieve, and inspect tool outputs and responses for sensitive data before returning or storing them.
- Log for review: Preserve the decision path, relevant evidence references, tool requests and approvals without copying secrets or entire sensitive payloads into logs.
Schema validation, CORS and prompt wording do not replace these controls. CORS is a browser policy, not an access boundary for non-browser clients. Keep credentials in deployment-managed secret storage where possible.
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Where should human approval fit in the incident lifecycle?
Use the agent to help people prepare, investigate and learn; do not present it as a replacement for an incident response capability. NIST SP 800-61 Rev. 3 integrates incident-response recommendations into cybersecurity risk management and the Cybersecurity Framework 2.0. The earlier SP 800-61 Rev. 2 guide, published in 2012, is superseded, so new designs should use Rev. 3 as their current NIST reference.
A useful division of responsibility is to let the agent summarize evidence, surface relevant stored context, identify uncertainty and propose options. People and deterministic controls should govern consequential containment or recovery. The approval rule should reflect the action’s impact and reversibility, the quality and provenance of its evidence, the caller’s authority and the organization’s incident policy. An agent’s confidence score, if one is used, is not a substitute for authorization.
What should I decide before deployment?
- Data boundaries: Which tenants, users and incidents may each API route, worker and agent tool access?
- Memory policy: Which facts are worth retaining, at what scope, for how long, and how can they be corrected or deleted?
- Workload behavior: Which jobs can be short and best-effort, and which require independent workers, retries and durable state?
- Action policy: Which operations are read-only, which are reversible, and which require explicit approval?
- Deployment behavior: How will workers handle shutdown, duplicate jobs, failed dependencies and recovery without losing the system’s durable record?
- Security and compliance: What data classification, audit, encryption, retention and access requirements apply in the actual deployment?
NIST SP 800-228 provides API protection guidance that can inform API security decisions. These choices should be made for the deployment’s threat model and obligations; neither a particular database nor an agent framework is mandated by the cited guidance.
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