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A production AI agent checklist for scoped access, safe writes, approval gates, traces, recovery, and repeatable evaluations.

Turn agent governance into enforceable rules for identity, actions, approvals, spend, data, models, tools, evidence, exceptions, and incidents.

Debug wrong-tool failures by freezing the visible tool set, inspecting traces, finding schema overlap, and testing selection separately from execution.

Recover AI agent workflows with error classes, idempotency, checkpoints, reconciliation, bounded retries, approval, cancellation, and compensation.

Sandbox agent-generated code with filesystem, process, network, credential, resource, artifact, lifetime, and cleanup controls matched to its threat model.

Secure AI agents with represented identity, downstream authorization, scoped credentials, approval gates, secret isolation, and useful audit records.

Build AI agent evaluations with representative tasks, deterministic checks, model judges, repeated runs, safety cases, and release gates.

Monitor AI agents across model calls, tool selection, policy checks, backend effects, retries, cost, quality, and recovery with one trace.

Choose agent compute by code trust, network access, lifetime, state, latency, and isolation: API call, job, process, container, sandbox, or VM.

Design tenant isolation for agent identity, databases, queues, caches, tools, artifacts, quotas, logs, credentials, and deletion paths.

Database patterns for agent tasks, idempotent operations, checkpoints, audit events, outboxes, tenant isolation, leases, and trace references.

Design persistent agent state for resumable tasks, human approvals, checkpoints, concurrent workers, schema changes, retention, and recovery.

Durable execution lets AI agents resume after crashes without repeating completed side effects. Learn checkpoints, idempotency, retries, and reconciliation.

Design an agent control plane for identity, policy, credentials, operations, approvals, audit records, limits, and resource ownership.

A practical AI agent architecture that separates the model loop, tools, identity, state, memory, durable execution, backend services, and telemetry.

Choose Composio for broad SaaS actions, Arcade for authenticated tool execution, RapidMCP for endpoint wrapping, or custom MCP for product-specific tasks.

A production MCP stack includes transport, protocol servers, authentication, tool routing, policy, execution credentials, operation state, and telemetry.

Safe infrastructure tools separate plan and apply, scope identity, protect credentials, require approval, reconcile retries, trace effects, and support recovery.

Keep provider keys out of prompts, tool arguments, results, URLs, browser code, and logs. Let the MCP server use separate scoped upstream credentials.

Reliable MCP tools have distinct task names, bounded schemas, scoped authorization, structured results, stable errors, safe writes, and selection evaluations.

Task-level agent tools state when to use them, when not to, required context, side effects, and results. Raw endpoint shapes rarely provide enough meaning.

Secure remote MCP servers with OAuth discovery, PKCE, resource indicators, audience-bound tokens, downstream authorization, and separate upstream credentials.

Wrap a REST API in an MCP server with task-level tools, validated schemas, scoped authentication, and structured errors.

Use an API or SDK for fixed application code and MCP when a model needs runtime tool discovery. Most production MCP tools call an API underneath.

MCP infrastructure includes the client, transport, server, tools, authorization, policy, credentials, backend execution, state, and telemetry around MCP.

AI agents need durable channels for approvals, completion notices, inbound work, and human replies, with identity, threading, delivery, and audit.

Codex and Windsurf can both call backend tools, but unattended tasks and IDE sessions need different approval, environment, retry, and handoff behavior.

Choose startup backend infrastructure by who operates it, required services, compliance, migration needs, failure recovery, and cost per completed workflow.

Use Claude Code and Cohesivity to inspect a project, create an ephemeral tenant, provision Postgres and hosting, test a waitlist API, and deploy it.

AI-generated apps still need server-side secrets, authorization, tenant isolation, safe writes, budgets, migrations, and recovery before real users arrive.

A coding agent can bootstrap one Cohesivity tenant, read live service contracts, provision database, auth, storage, and hosting, then verify each result.

Choose a Cursor backend by project creation, MCP and terminal support, permission scope, idempotent writes, development isolation, and deployment handoff.

Choose a Claude Code backend by whether its CLI or MCP path can create a scoped development project, recover writes, and deploy without leaking provider keys.

An agent-operable backend exposes provisioning, status, credentials, safe writes, approvals, cleanup, and structured failures through callable contracts.

Compare backends for AI coding agents by project creation, authentication, tool scope, safe writes, hosting, ownership, and production access.

Calculate AI agent cost per completed task across model tokens, paid tools, runtime, storage, retries, and idle resources instead of tracking one API bill.

Production AI agents need scoped access, bounded runs, safe writes, durable recovery, sandboxing, traces, evaluations, and a tested stop path.

The AI agent stack separates reasoning, tools, identity, memory, execution, application infrastructure, and telemetry so each failure has one owner.

AI agents can use conventional backend services, but they need structured provisioning, scoped credentials, safe retries, and machine-readable failures.

AI agents choose steps at runtime, while SaaS applications execute workflows designed in advance. That changes permissions, testing, interfaces, and costs.

AI agent infrastructure combines models, tools, identity, state, execution, application backends, and telemetry. Here is what each layer owns.

Agent-native infrastructure lets an AI agent discover, provision, and operate backend resources through structured, permissioned interfaces.