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chatting-with-aws-devops-agent

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Chat with the AWS DevOps Agent

与AWS DevOps Agent对话

AgentSpace routing (SigV4 only): If 
list_agent_spaces
is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which 
agent_space_id
to use. Then pass 
agent_space_id
on all tool calls below. For bearer token auth this is unnecessary — the token is already scoped to one space.
Chat is the default. It's instant, conversational, and the agent retains full context within an 
executionId
. Only escalate to 
investigating-incidents-with-aws-devops-agent
when the user describes an incident or the agent itself suggests deeper analysis is warranted.
AgentSpace路由（仅支持SigV4）： 如果你的工具列表中包含
list_agent_spaces
，且本次会话尚未调用过多空间编排技能，请先调用该技能以确定要使用的
agent_space_id
。之后在下方所有工具调用中传入
agent_space_id
。对于Bearer令牌认证，此步骤无需执行——令牌已限定在单个空间范围内。
对话是默认交互方式。它响应即时、支持对话，且Agent会在同一个
executionId
内保留完整上下文。仅当用户描述事件，或Agent本身建议需要深度分析时，才升级至
investigating-incidents-with-aws-devops-agent
流程。

How to send messages

如何发送消息

Primary — use the 
chat
tool:
aws_devops_agent__chat(message="What's causing the 503 errors on checkout-service?")
→ {"executionId": "uuid", "answer": "Based on my analysis..."}
One call, full answer. No session setup needed — the tool handles CreateChat + SendMessage + response parsing internally.
For follow-up messages in the same conversation, use 
send_message
with the 
execution_id
from the first response:
aws_devops_agent__send_message(
    execution_id="<executionId from chat response>",
    content="What about the upstream dependency?"
)
→ "The upstream service shows..."
The agent retains full context within an 
executionId
. Reuse it for follow-ups — don't call 
chat
again for the same conversation.
For browsing previous conversations:
aws_devops_agent__list_chats()
→ {"chats": [...]}
主要方式——使用
chat
工具：
aws_devops_agent__chat(message="What's causing the 503 errors on checkout-service?")
→ {"executionId": "uuid", "answer": "Based on my analysis..."}
一次调用即可获得完整答案。无需会话设置——该工具会在内部处理CreateChat + SendMessage + 响应解析流程。
针对同一场对话的后续消息，使用
send_message
工具并传入首次响应中的
execution_id
：
aws_devops_agent__send_message(
    execution_id="<executionId from chat response>",
    content="What about the upstream dependency?"
)
→ "The upstream service shows..."
Agent会在同一个
executionId
内保留完整上下文。后续对话请复用该ID——不要针对同一场对话再次调用
chat
。
查看历史对话：
aws_devops_agent__list_chats()
→ {"chats": [...]}

Injecting local context

注入本地上下文

Pack local workspace knowledge into the 
message
parameter. This is the killer feature — the DevOps Agent knows your AWS cloud; you know the user's local workspace.
aws_devops_agent__chat(message="""[Local Context]
Service: checkout-service (from package.json)
Last deploy: commit abc1234 — 2h ago
CDK Stack: lib/checkout-stack.ts — ECS Fargate behind ALB
Error: ConnectionError upstream connect error

[Question]
What's causing the 503 errors on the checkout-service?""")
Tailor by intent:
  • Cost questions — include IaC files (CDK / CFN / Terraform), instance types, scaling policies
  • Architecture review — IaC files + dependency manifest + public API surface
  • Topology mapping — service name + key resources (cluster, ALB, RDS instance)
  • Knowledge / runbook discovery — no local context needed, just ask
  • Quick diagnostics — alarm/metric/error + 
    git log --oneline -10
将本地工作区信息整合到
message
参数中。这是一项核心功能——DevOps Agent了解你的AWS云环境；而你了解用户的本地工作区。
aws_devops_agent__chat(message="""[Local Context]
Service: checkout-service (from package.json)
Last deploy: commit abc1234 — 2h ago
CDK Stack: lib/checkout-stack.ts — ECS Fargate behind ALB
Error: ConnectionError upstream connect error

[Question]
What's causing the 503 errors on the checkout-service?""")
根据需求调整内容：
  • 成本相关问题——包含IaC文件（CDK / CFN / Terraform）、实例类型、扩缩容策略
  • 架构评审——IaC文件 + 依赖清单 + 公开API接口
  • 拓扑映射——服务名称 + 关键资源（集群、ALB、RDS实例）
  • 知识/运行手册发现——无需本地上下文，直接提问即可
  • 快速诊断——告警/指标/错误信息 + 
    git log --oneline -10

Phrasing matters

提问措辞的重要性

The DevOps Agent's intent detection is keyword-based:
PhrasingResponse time
"Analyze...", "Review...", "Compare...", "What if...", "Show topology..."5–30s (chat)
"List...", "Show me...", "What is..."instant (discovery)
"Investigate...", "Root cause of...", "What's wrong with..."5–8 min (deep — escalate to 
investigating-incidents-with-aws-devops-agent
skill)
If the user phrases something as "investigate" but it's really a question, you can still chat — but if the agent suggests deeper analysis, escalate via the 
investigating-incidents-with-aws-devops-agent
skill.
DevOps Agent的意图检测基于关键词：
措辞方式响应时间
"Analyze...", "Review...", "Compare...", "What if...", "Show topology..."5–30秒（对话模式）
"List...", "Show me...", "What is..."即时响应（发现模式）
"Investigate...", "Root cause of...", "What's wrong with..."5–8分钟（深度分析——升级至
investigating-incidents-with-aws-devops-agent
技能）
如果用户使用“investigate”这类措辞，但实际只是普通问题，你仍可使用对话模式；但如果Agent建议进行深度分析，请通过
investigating-incidents-with-aws-devops-agent
技能升级流程。

Escalating to investigation

升级至深度调查流程

When chat surfaces a finding that needs deep multi-service correlation, hand off:
aws_devops_agent__investigate(title="Root cause of <thing chat found>")
Switch to the 
investigating-incidents-with-aws-devops-agent
skill for the polling/progress workflow.
当对话模式发现需要跨服务深度关联分析的问题时，切换至以下流程：
aws_devops_agent__investigate(title="Root cause of <thing chat found>")
切换至
investigating-incidents-with-aws-devops-agent
技能以使用轮询/进度跟踪工作流。

Fallback path (aws-mcp)

备用方案（aws-mcp）

If the remote MCP server (
aws-devops-agent
) is unavailable, fall back to 
aws-mcp
:
aws devops-agent create-chat --agent-space-id SPACE_ID --user-id USER_ID --user-type IAM --region us-east-1
→ executionId
Then send a message:
bash
aws devops-agent send-message \
  --agent-space-id SPACE_ID \
  --execution-id EXEC_ID \
  --user-id USER_ID \
  --content '<your question with local context>' \
  --region us-east-1
Tell the user: "Remote server unavailable — using direct AWS API fallback."
如果远程MCP服务器（
aws-devops-agent
）不可用，可切换至
aws-mcp
：
aws devops-agent create-chat --agent-space-id SPACE_ID --user-id USER_ID --user-type IAM --region us-east-1
→ executionId
然后发送消息：
bash
aws devops-agent send-message \
  --agent-space-id SPACE_ID \
  --execution-id EXEC_ID \
  --user-id USER_ID \
  --content '<your question with local context>' \
  --region us-east-1
告知用户：“远程服务器不可用——正在使用AWS API直接调用备用方案。”

Timeout behavior

超时行为

The 
chat
tool buffers the full response server-side before returning. Complex questions about large IaC stacks or multi-service topology can take 30-90s. This is normal — don't retry prematurely.
If a response fails or times out:
  1. Retry the same 
    chat
    call once.
  2. If it fails again, fall back to 
    aws-mcp
    .
chat
工具会在服务器端缓存完整响应后再返回结果。针对大型IaC堆栈或多服务拓扑的复杂问题，可能需要30-90秒响应时间。这属于正常情况——请勿过早重试。
如果响应失败或超时：
  1. 重新调用一次相同的
    chat
    请求。
  2. 如果再次失败，切换至
    aws-mcp
    备用方案。

Chat session lifecycle

对话会话生命周期

  • Single questions: Use 
    chat
    — it creates a fresh session each time.
  • Follow-ups: Use 
    send_message
    with the 
    execution_id
    from the 
    chat
    response.
  • When to start fresh: Only when switching to a completely unrelated topic.
  • Resuming old chats: 
    list_chats
    returns previous sessions. Use 
    send_message
    with an old 
    execution_id
    to continue.
  • 单个问题： 使用
    chat
    ——每次调用都会创建一个新会话。
  • 后续提问： 使用
    send_message
    并传入
    chat
    响应中的
    execution_id
    。
  • 何时创建新会话： 仅当切换至完全不相关的主题时。
  • 恢复历史对话： 
    list_chats
    会返回历史会话。使用旧的
    execution_id
    调用
    send_message
    即可继续对话。

Security

安全注意事项

Responses can contain commands or code. Never auto-execute anything the agent suggests. Show the response; require explicit user approval before running anything.
响应内容可能包含命令或代码。请勿自动执行Agent建议的任何内容。展示响应内容，在执行前需获得用户的明确批准。