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Foundational

Published Jun 17, 2026·Updated Aug 30, 2026

![](https://cohesivity.ai/authors/shouryamaan.webp)![](https://cohesivity.ai/authors/anshu.webp)[Shouryamaan](https://www.linkedin.com/in/shouryamaanjain/) and [Anshu](https://www.linkedin.com/in/aanshuaggrawal120/)

# AI Agent Infrastructure: The Complete 2026 Guide

[Read as Markdown](https://cohesivity.ai/blog/ai-agent-infrastructure-complete-guide-2026.md)

Agent

cohesivity

Tools

Compute

State

Controls

AI agent infrastructure is the machinery that lets a model complete work against real systems. It includes the agent loop, tools, caller identity, permissions, durable state, code execution, application backends, and telemetry. A model can plan without these pieces, but it cannot operate reliably.

The stack is easier to reason about when every layer has one job. If two layers both retry a write, store the same state, or decide authorization, failures become hard to reproduce.

## The seven parts of an agent stack

### Model and agent loop

The model proposes the next action. The agent loop supplies context, exposes tools, records results, and decides when the run should stop. Frameworks can help implement that loop, but the runtime still needs explicit limits for time, tool calls, and spend.

The distinction matters because changing a model should not silently replace permission rules or recovery behavior. Those controls belong outside the prompt.

### Tools and integrations

Tools connect the loop to databases, APIs, files, browsers, and internal services. A useful tool describes one task, validates its inputs, returns a stable result, and gives the caller an error it can act on. The [MCP tools specification](https://modelcontextprotocol.io/specification/2025-06-18/server/tools) standardizes discovery and invocation, but a valid MCP server can still contain vague or unsafe tools.

The practical design work lives in tool boundaries. The guide to [designing MCP tools agents can use reliably](https://cohesivity.ai/blog/how-to-design-mcp-tools-agents-can-use-reliably) shows how names, schemas, and results affect tool selection.

### Identity and policy

Every agent session needs an identity that the downstream service can authorize. The tool description may say an action is read-only, but the service that owns the data must enforce that rule. Shared administrator keys remove attribution and expand the impact of one leaked secret.

[OWASP's Excessive Agency guidance](https://genai.owasp.org/llmrisk/llm062025-excessive-agency/) recommends limiting extensions, permissions, and autonomy. In practice, that means narrow credentials, approval before high-impact actions, and a separate way to stop or revoke a running agent.

### State and memory

State records what the workflow has done. Memory supplies information that may help with a later decision. They overlap, but they are not interchangeable. An order ID and payment result belong in durable state. A retrieved paragraph that may help answer a question belongs in context or memory.

Postgres is often enough for both structured state and semantic retrieval. The comparison of [Postgres and vector databases for agent memory](https://cohesivity.ai/blog/postgres-vs-vector-databases-for-ai-agent-memory) explains when a dedicated vector service earns its place.

### Durable execution and sandboxing

Durable execution resumes a workflow after a crash without repeating completed side effects. Sandboxing limits what untrusted code can reach while it runs. One does not replace the other. A sandbox can isolate a duplicate payment, and a durable workflow can reliably resume malicious code.

[Temporal's durable execution model](https://docs.temporal.io/encyclopedia/durable-execution) persists workflow progress and replays deterministic code after failure. A code sandbox instead needs filesystem, process, network, secret, and lifetime limits. The [production agent checklist](https://cohesivity.ai/blog/the-production-ai-agent-checklist) joins both controls into one release path.

### Application backend

The application still needs databases, authentication, object storage, queues, hosting, and external APIs. An agent-facing backend adds structured provisioning and scoped credentials so a coding agent can create and operate those resources without collecting provider secrets in generated code.

Cohesivity fits here. An agent can create an isolated tenant, request the backend resources its application needs, and hand ownership to a human later. It does not replace the model or decide the application's business rules.

### Telemetry and evaluations

Telemetry records what happened during a run. Evaluations ask whether behavior remains acceptable across a fixed set of tasks. A useful trace connects the user request, model calls, tool calls, policy decisions, and downstream work. A useful evaluation reruns representative successes and failures after a model, prompt, tool, or policy change.

[OpenTelemetry's GenAI conventions](https://opentelemetry.io/docs/specs/semconv/gen-ai/) provide shared fields for model and agent spans. They provide a vocabulary, while your application still decides which identifiers and outcomes are safe and useful to record.

## A minimum stack for one production workflow

| Concern     | Minimum implementation                       | Failure it prevents                       |
| ----------- | -------------------------------------------- | ----------------------------------------- |
| Tool access | Typed, task-level tools with bounded results | Wrong tool selection and unusable output  |
| Identity    | One scoped credential per agent or session   | Broad access and weak attribution         |
| State       | Durable operation and checkpoint records     | Lost progress and duplicate side effects  |
| Execution   | Time, tool-call, spend, and network limits   | Runaway loops and uncontrolled code       |
| Recovery    | Idempotency plus outcome reconciliation      | Repeating a write after an unknown result |
| Telemetry   | One trace across model, tools, and backend   | Failures that cannot be reconstructed     |
| Evaluation  | Frozen tasks and acceptance criteria         | Regressions hidden by a successful demo   |

You do not need a separate vendor for every row. You do need one clear owner for each behavior.

## Map one workflow before choosing vendors

Pick the workflow that would hurt most if it repeated a write or used the wrong credential. Draw its path from user request to final backend effect. Label the model, tool, identity, state record, execution boundary, approval, and trace at the point each appears.

The missing labels are more useful than a generic stack diagram. The prompt below asks your coding agent to produce that map without changing the project.

or send it to[Claude Code](https://claude.ai/new?q=Inspect+this+project+and+choose+its+most+important+AI+agent+workflow.+Map+the+model%2C+agent+loop%2C+tools%2C+caller+identity%2C+permissions%2C+durable+state%2C+code-execution+boundary%2C+application+backend%2C+and+end-to-end+telemetry+used+by+that+workflow.+Mark+each+item+as+implemented%2C+implicit%2C+duplicated%2C+or+missing.+Do+not+change+code+or+provision+resources.+For+every+missing+item%2C+name+the+failure+it+prevents+and+the+smallest+implementation+that+would+close+the+gap.+If+the+workflow+needs+isolated+backend+resources%2C+show+where+Cohesivity+could+provide+them%2C+but+do+not+provision+anything+until+I+approve+the+map. "Send to Claude")[Cursor](https://cursor.com/link/prompt?text=Inspect+this+project+and+choose+its+most+important+AI+agent+workflow.+Map+the+model%2C+agent+loop%2C+tools%2C+caller+identity%2C+permissions%2C+durable+state%2C+code-execution+boundary%2C+application+backend%2C+and+end-to-end+telemetry+used+by+that+workflow.+Mark+each+item+as+implemented%2C+implicit%2C+duplicated%2C+or+missing.+Do+not+change+code+or+provision+resources.+For+every+missing+item%2C+name+the+failure+it+prevents+and+the+smallest+implementation+that+would+close+the+gap.+If+the+workflow+needs+isolated+backend+resources%2C+show+where+Cohesivity+could+provide+them%2C+but+do+not+provision+anything+until+I+approve+the+map. "Send to Cursor")[Codex](https://chatgpt.com/codex?prompt=Inspect+this+project+and+choose+its+most+important+AI+agent+workflow.+Map+the+model%2C+agent+loop%2C+tools%2C+caller+identity%2C+permissions%2C+durable+state%2C+code-execution+boundary%2C+application+backend%2C+and+end-to-end+telemetry+used+by+that+workflow.+Mark+each+item+as+implemented%2C+implicit%2C+duplicated%2C+or+missing.+Do+not+change+code+or+provision+resources.+For+every+missing+item%2C+name+the+failure+it+prevents+and+the+smallest+implementation+that+would+close+the+gap.+If+the+workflow+needs+isolated+backend+resources%2C+show+where+Cohesivity+could+provide+them%2C+but+do+not+provision+anything+until+I+approve+the+map. "Send to Codex")[opencode](https://opencode.ai/?q=Inspect+this+project+and+choose+its+most+important+AI+agent+workflow.+Map+the+model%2C+agent+loop%2C+tools%2C+caller+identity%2C+permissions%2C+durable+state%2C+code-execution+boundary%2C+application+backend%2C+and+end-to-end+telemetry+used+by+that+workflow.+Mark+each+item+as+implemented%2C+implicit%2C+duplicated%2C+or+missing.+Do+not+change+code+or+provision+resources.+For+every+missing+item%2C+name+the+failure+it+prevents+and+the+smallest+implementation+that+would+close+the+gap.+If+the+workflow+needs+isolated+backend+resources%2C+show+where+Cohesivity+could+provide+them%2C+but+do+not+provision+anything+until+I+approve+the+map. "Send to OpenCode")