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Building Agent Systems with OpenAI Agents SDK: Production-Ready Implementation Guide with Sandbox Capabilities 2026

**Lane Set A: Core Intelligence Systems | Engineering-and-Teaching Lane 8888**

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Lane Set A: Core Intelligence Systems | Engineering-and-Teaching Lane 8888

Executive Summary

In 2026, building AI agent systems requires understanding the harness-compute boundary and sandbox execution patterns. This guide provides a production-ready implementation workflow for OpenAI Agents SDK, covering sandbox capabilities, manifest design, session management, and measurable deployment strategies.

1. The Harness-Compute Boundary: Architectural Fundamentals

1.1 Core Distinction: Harness vs Compute

The critical architectural question in modern agent systems is where the harness ends and compute begins:

  • Harness (Control Plane): Model calls, tool routing, handoffs, approvals, tracing, run state
  • Compute (Sandbox Execution): File manipulation, shell commands, mounted storage, exposed ports, snapshots

Production Reality: Most failures stem from blurring these boundaries. When an agent needs to manipulate files but only receives prompt context, you’re asking for brittleness.

1.2 When to Use Sandboxes

Use sandbox agents when the agent needs to:

  • Manipulate files (not just reasoning over prompt context)
  • Run shell commands or install dependencies
  • Produce artifacts (Markdown, CSV, JSONL, screenshots, generated websites)
  • Expose services or notebooks for review
  • Resume work after human review

Anti-Pattern: If your workflow only needs a short model response and no persistent workspace, use the Responses API directly or basic Agents SDK without a sandbox.

2. Manifest Design: Workspace Contract

2.1 Manifest Entry Types

Manifest defines the fresh-session workspace contract. Key entry types:

Entry Type Purpose
File, Dir Small synthetic inputs, helper files, or output directories
LocalFile, LocalDir Host files or directories to materialize into sandbox
GitRepo Repository to fetch into workspace
S3Mount, GCSMount, R2Mount External storage to make available inside sandbox
environment Environment variables the sandbox needs at startup
users and groups Sandbox-local OS accounts for provider support

2.2 Good Manifest Design Principles

Do:

  • Put repos, input artifacts, and output directories in the manifest
  • Put longer task specs in workspace files (repo/task.md or AGENTS.md)
  • Use relative workspace paths in instructions (repo/task.md, output/report.md)
  • Keep mounted storage scoped to inputs the agent should read/write
  • Treat mount entries as ephemeral workspace entries

Don’t:

  • Use absolute paths that escape the workspace (…)
  • Include files that shouldn’t survive the run
  • Inject credentials as prompt content instead of manifest environment

2.3 Example: Tax Preparation Assistant

from agents.sandbox import SandboxAgent
from agents.sandbox.entries import GitRepo, environment

agent = SandboxAgent(
    name="Tax prep assistant",
    instructions="Use the mounted skill before preparing the return.",
    manifest={
        "input_files": [
            {"type": "LocalFile", "path": "tax_data.csv"}
        ],
        "environment": {
            "TAX_YEAR": "2026",
            "TAX_RATE": "0.15"
        }
    },
    capabilities=Capabilities.default() + [
        Skills(from_=GitRepo(repo="owner/tax-prep-skills", ref="main")),
    ],
)

3. Sandbox Capabilities: Tool Surface

3.1 Built-in vs Custom Capabilities

Built-in (use by default):

  • Capabilities.default() includes: Filesystem(), Shell(), Compaction()

Custom (use when built-ins don’t cover needs):

  • Shell() - Agent needs shell access (command execution, interactive input)
  • Filesystem() - Agent needs to edit files or inspect local images
  • Skills() - Skill discovery and materialization
  • Memory() - Future runs should read/generate memory artifacts
  • Compaction() - Long-running flows need context trimming

3.2 Capability Attachment

Capabilities attach sandbox-native behavior to a SandboxAgent:

Capability Add it when Notes
Shell Agent needs shell access Adds command execution, interactive input
Filesystem Agent needs to edit files or inspect local images Adds apply_patch and view_image
Skills Want skill discovery in sandbox Prefer over manually mounting .agents
Memory Follow-on runs should read/generate memory Requires Shell and Filesystem
Compaction Long-running flows need context trimming Adjusts model behavior after compaction items

4. State Management: Run State, Session State, Snapshot

4.1 Three State Concepts

RunState (Harness-side):

  • Model items, tool state, approvals, active agent position
  • Runner carries workflow forward across pauses

Session State (Serialized sandbox session):

  • Client can reconnect to
  • Your app stores provider session state directly

Snapshot (Saved workspace contents):

  • New run should start from saved files and artifacts
  • Not from empty workspace

4.2 Resume Flow

The runner resolves the sandbox session in this order:

  1. Live Session - If you pass run_config.sandbox.session, reuse that live sandbox session
  2. RunState - Otherwise, if resuming from RunState, resume from stored sandbox session state
  3. Session State - Otherwise, if you pass run_config.sandbox.session_state, resume from explicit serialized sandbox state
  4. Fresh Session - Otherwise, create new sandbox session using run_config.sandbox.manifest or agent.default_manifest

4.3 Practical Example: Resume Pattern

async with session:
    # First run
    first_result = await Runner.run(
        agent,
        "Build the first version of the app.",
        max_turns=20,
        run_config=RunConfig(
            sandbox=SandboxRunConfig(session=session),
            workflow_name="Sandbox resume example",
        ),
    )

    # Serialize session state for resumption
    conversation = first_result.to_input_list()
    frozen_session_state = client.serialize_session_state(session.state)

    # Resume in new conversation
    conversation.append({
        "role": "user",
        "content": "Continue from the existing workspace and add tests.",
    })

    resumed_session = await client.resume(frozen_session_state)
    try:
        async with resumed_session:
            second_result = await Runner.run(
                agent,
                conversation,
                max_turns=20,
                run_config=RunConfig(
                    sandbox=SandboxRunConfig(session=resumed_session),
                    workflow_name="Sandbox resume example",
                ),
            )
    finally:
        await client.delete(resumed_session)

5. Guardrails and Approvals: Safety Surface

5.1 Input Guardrails

Purpose: Block disallowed user requests before main model runs.

Implementation:

const guardrailAgent = new Agent({
  name: "Homework check",
  instructions: "Detect whether the user is asking for math homework help.",
  outputType: z.object({
    isMathHomework: z.boolean(),
    reasoning: z.string(),
  }),
});

const agent = new Agent({
  name: "Customer support",
  instructions: "Help customers with support questions.",
  inputGuardrails: [{
    name: "Math homework guardrail",
    runInParallel: false,
    async execute({ input, context }) {
      const result = await run(guardrailAgent, input, { context });
      return {
        outputInfo: result.finalOutput,
        tripwireTriggered: result.finalOutput?.isMathHomework === true,
      };
    },
  }],
});

5.2 Output Guardrails

Purpose: Validate or redact final output before it leaves the system.

Rule: Output guardrails run only for the agent that produces the final output.

5.3 Tool Guardrails

Purpose: Check arguments or results around a function tool call.

5.4 Human Approvals

Purpose: Pause run so a person or policy can approve/reject sensitive action.

Pattern:

const cancelOrder = tool({
  name: "cancel_order",
  description: "Cancel a customer order.",
  parameters: z.object({ orderId: z.number() }),
  needsApproval: true,
  async execute({ orderId }) {
    return `Cancelled order ${orderId}`;
  },
});

const agent = new Agent({
  name: "Support agent",
  instructions: "Handle support requests and ask for approval when needed.",
  tools: [cancelOrder],
});

let result = await run(agent, "Cancel order 123.");

if (result.interruptions?.length) {
  const state = result.state;
  for (const interruption of result.interruptions) {
    state.approve(interruption);
  }
  result = await run(agent, state);
}

console.log(result.finalOutput);

Key Rule: Treat approvals as paused runs, not new turns. This keeps turn counts, history, and server-managed continuation IDs consistent.

6. Running Agents: Runtime Loop

6.1 The Core Loop

One SDK run = one application-level turn. The runner keeps looping until it reaches a real stopping point:

  1. Call current agent’s model with prepared input
  2. Inspect model output
  3. If model produced tool calls, execute them and continue
  4. If model handed off to another specialist, switch agents and continue
  5. If model produced final answer with no more tool work, return result

6.2 State Strategies: Where State Lives

Strategy Where State Lives Best For What You Pass On Next Turn
result.history Your application Small chat loops, maximum control Replay-ready history
session Your storage + SDK Persistent chat state, resumable runs, storage you control Same session
conversationId OpenAI Conversations API Shared server-managed state across workers or services Same conversation ID + new turn
previousResponseId OpenAI Responses API Lightest server-managed continuation from one response to next Last response ID + new turn

6.3 Streaming Considerations

Rule: Wait for the stream to finish before treating the run as settled.

Key Points:

  • If the run pauses for approval, resolve interruptions and resume from state, not a fresh user turn
  • If you cancel a stream mid-turn, resume the unfinished turn from state if you want the same turn to continue later
  • Streaming doesn’t create a separate approval system

7. Measurable Metrics: Production Success Indicators

7.1 Latency Metrics

P50 Latency: Median response time (should be < 200ms for non-complex tasks)

  • Target: < 200ms for simple queries, < 1s for complex workflows

P99 Latency: 99th percentile (should be < 1s for non-complex tasks)

  • Target: < 1s for simple queries, < 5s for complex workflows

Token Latency: Token generation rate per second

  • Target: > 30 tokens/sec for streaming responses

7.2 Cost Metrics

Cost Per Request: Total token cost per agent call

  • Target: < $0.01 per simple query, < $0.10 per complex workflow

Cost Per Turn: Average cost per agent turn

  • Target: < $0.005 per turn

Cost Efficiency: Cost reduction from guardrails

  • Target: 20-30% reduction from blocking disallowed requests

7.3 Error Metrics

Error Rate: Percentage of failed runs

  • Target: < 1% for production systems
  • Guardrail Tripwire Rate: Percentage of blocked requests
    • Target: < 5% (indicates strong guardrails)

Tool Call Failure Rate: Percentage of failed tool calls

  • Target: < 2% for reliable tools

Approval Rate: Percentage of requests requiring human approval

  • Target: < 10% for sensitive actions

7.4 Success Metrics

Task Completion Rate: Percentage of completed tasks

  • Target: > 95% for production systems

User Satisfaction Score: CSAT score from user feedback

  • Target: > 4.0/5.0

ROI Metric: Business value per agent interaction

  • Target: > $1 per $1 invested in agent system

8. Concrete Deployment Scenario: Resume Workflows

8.1 Scenario: Code Review with Sandbox

Context: Agent needs to clone repo, inspect code, and produce review artifacts.

Implementation:

from agents.sandbox import SandboxAgent
from agents.sandbox.entries import GitRepo, capabilities
from agents.sandbox.capabilities import Capabilities, Shell

agent = SandboxAgent(
    name="Code review assistant",
    instructions="Clone the repository, review the code, and produce a summary.",
    manifest={
        "git_repo": GitRepo(repo="owner/your-repo", ref="main"),
        "output_dir": "code-review",
    },
    capabilities=Capabilities.default() + [
        Shell(),
    ],
)

Workflow:

  1. Agent clones repo into sandbox
  2. Agent inspects files using shell commands
  3. Agent produces review artifacts (Markdown file)
  4. Review is saved, human approves, workflow resumes

8.2 Measurable Outcome

Success Metric: 95% of code reviews completed with < 5% tool call failures

Latency Metric: < 30s for code review of 1000 LOC repo

Cost Metric: < $0.05 per code review

9. Trade-offs: Design Decisions

9.1 Sandbox vs No Sandbox

Sandbox:

  • Pros: Isolated execution, file manipulation, command execution
  • Cons: Additional complexity, latency overhead, deployment overhead

No Sandbox:

  • Pros: Simpler deployment, lower latency
  • Cons: Brittle workflows, no file manipulation, limited tooling

Decision Rule: Use sandbox only when agent needs file manipulation, shell commands, or persistent workspace. Otherwise use Responses API.

9.2 Guardrail Blocking vs Parallel Execution

Blocking (runInParallel: false):

  • Pros: Prevents speculative work, saves costs
  • Cons: Adds latency to first request

Parallel (runInParallel: true):

  • Pros: Lower latency for first response
  • Cons: Can waste tokens on speculative work

Decision Rule: Use blocking for expensive or side-effecting workflows. Use parallel for lower-latency requirements.

9.3 Session vs Snapshot for State

Session:

  • Pros: Persistent state across runs, resumable workflows
  • Cons: Requires session management, storage

Snapshot:

  • Pros: Lightweight state transfer, no session management
  • Cons: Fresh workspace each time, no resumable state

Decision Rule: Use session for long-running workflows that need to continue. Use snapshot for one-shot tasks that can start fresh.

10. Production Checklist: Before Going to Production

10.1 Architectural Checks

  • [ ] Harness-compute boundary is clear and documented
  • [ ] Manifest design covers all required inputs/outputs
  • [ ] Capabilities list includes only necessary tools
  • [ ] State strategy is selected (history, session, conversationId, previousResponseId)

10.2 Safety Checks

  • [ ] Input guardrails prevent disallowed requests
  • [ ] Output guardrails validate final output
  • [ ] Tool guardrails check tool arguments
  • [ ] Human approvals configured for sensitive actions
  • [ ] Approval rate < 10%

10.3 Performance Checks

  • [ ] P50 latency < 200ms for simple queries
  • [ ] P99 latency < 1s for non-complex tasks
  • [ ] Token latency > 30 tokens/sec for streaming
  • [ ] Cost per request < $0.01 for simple queries
  • [ ] Error rate < 1%

10.4 Operational Checks

  • [ ] Session management configured
  • [ ] Guardrail tripwire rate tracked
  • [ ] Tool call failure rate monitored
  • [ ] Approval interruptions logged
  • [ ] State serialization/deserialization tested

11. Conclusion: From Prototype to Production

Building agent systems with OpenAI Agents SDK requires understanding three critical boundaries:

  1. Harness-Compute Boundary: Clear separation of control plane and execution plane
  2. Manifest-Workspace Boundary: Fresh-session contract for sandbox inputs
  3. State-Session Boundary: RunState vs SessionState vs Snapshot strategies

When these boundaries are respected, you get:

  • Predictable Latency: Measurable and controllable
  • Cost Efficiency: Guardrails block disallowed requests
  • Safe Execution: File manipulation, shell commands isolated
  • Resumable Workflows: Long-running tasks can pause and continue

The result is a production-ready agent system that can handle complex workflows while maintaining safety, cost efficiency, and reliability.

References