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Agentcore Observability Setup

Organization
aws
agentcore-observability-setup

Validates and bootstraps Amazon Bedrock AgentCore observability so customers can trace agent reasoning, detect silent failures, and measure performance before an outage. Use this skill when a user asks to check, validate, audit, verify, fix, or set up AgentCore observability, tracing, metrics, or logging - for example "is AgentCore observability configured correctly", "why can't I see traces for my agent", "my Bedrock agent has no spans in CloudWatch", "validate agent monitoring", "AgentCore observability readiness", or "set up OTEL for my agent". Covers AgentCore Runtime agents, Memory and Gateway resources, built-in tools, and agents hosted outside the runtime (Lambda, ECS, EKS, on-prem, multi-cloud). Verifies what is reachable via read-only CloudWatch, X-Ray, and AgentCore control-plane APIs, and prescribes exact remediation for gaps it cannot directly read such as code-level OTEL instrumentation.

Overview

Publisheraws
Repositorytools-for-devops-agent
Skill nameagentcore-observability-setup
Stars
120
Forks
97
Bundled files
9
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aws on GitHub. Read the source before you install it.

Installation

Install the Agentcore Observability Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/aws/tools-for-devops-agent/tree/main/skills/agentcore-observability-setup
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aws/tools-for-devops-agent.git /tmp/tools-for-devops-agent
mkdir -p .claude/skills
cp -r /tmp/tools-for-devops-agent/skills/agentcore-observability-setup .claude/skills/agentcore-observability-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agentcore Observability Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agentcore Observability Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agentcore Observability Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

AgentCore Observability Setup & Validation

Validate and bootstrap observability for Amazon Bedrock AgentCore workloads, aligned with the AgentCore observability documentation. Produce a readiness report that states, per check, what is configured vs. what should be, and generate the exact remediation for every gap.

Operating Principle

Verify where reachable; prescribe everywhere else. Every check is exactly one of:

  • VERIFY — confirmable through a read-only API (log group exists, data arriving, spans flowing, Transaction Search enabled, tracing toggle, delivery configured).
  • PRESCRIBE — cannot be read directly (code-level OTEL instrumentation in the agent's source). Validate the effect (are spans arriving?) and emit the exact steps. Do not claim to read source code.

This skill is read-only. It validates and generates configuration/commands. The customer decides whether to apply them. Never mutate IAM, resources, or configuration.

When to Use

Activate when the user asks to:

  • Check / validate / audit whether AgentCore observability is configured correctly
  • Diagnose missing traces, spans, metrics, or logs for a Bedrock agent
  • Set up or bootstrap observability (tracing, metrics, logging) for an agent
  • Run an AgentCore observability readiness review
  • Understand why agent telemetry isn't appearing in CloudWatch GenAI Observability

Step 1: Detect Permission Tier

This skill degrades gracefully based on which read-only permissions the DevOps Agent role holds. Detect the tier by attempting calls and noting failures; run the checks the tier allows and report what to add to unlock the rest. Never attempt to modify IAM.

TierPermissionsUnlocks
1 — Standard (default DA IAM)logs:DescribeLogGroups, logs:FilterLogEvents, logs:GetLogEvents, logs:StartQuery, logs:GetQueryResults, cloudwatch:GetMetricData, cloudwatch:ListMetrics, cloudwatch:DescribeAlarmsTelemetry-arrival verification for any host
2 — Runtime configbedrock-agentcore:GetAgentRuntime, bedrock-agentcore:ListAgentRuntimes, xray:GetTraceSegmentDestination, logs:DescribeDeliveries, logs:DescribeDeliverySources, logs:DescribeDeliveryDestinations, logs:DescribeResourcePoliciesRuntime tracing/env, Transaction Search state, Memory/Gateway delivery, X-Ray resource policy
3 — Non-runtime hostlambda:GetFunctionConfiguration; ecs:DescribeTaskDefinition, ecs:DescribeServices, ecs:ListTasks; eks:DescribeClusterHost-side config verification for Lambda / ECS / EKS

If Tier 2/3 permissions are absent, tell the user the check is prescriptive-only here and give the exact scoped inline policy to attach to the DevOps Agent role (see references/iam-tiers.md) so a re-run can verify it. The shared managed policy is cross-tenant and must not be modified per-skill.

Step 2: Determine Scope (Host & Surface)

Host detection:

  • Runtime — auto-detect with bedrock-agentcore:GetAgentRuntime / bedrock-agentcore:ListAgentRuntimes (Tier 2). If absent, ask the user.
  • Non-runtime — the agent cannot reliably auto-detect the host. Ask: "Is the agent on Lambda, ECS, EKS, or on-prem/another cloud?"

Surfaces to assess (ask which apply, or discover via Tier 2 List*): Runtime agents · Memory resources · Gateway resources · Built-in tools (code interpreter, browser) · non-runtime host.

Outcome matrix:

Host / SurfaceExpected outcome
Runtime agentVerify (Tier 1+2) + Prescribe
Memory resourceVerify delivery + tracing (Tier 2) + Prescribe
Gateway resourceVerify delivery + tracing (Tier 2) + Prescribe
Built-in toolsPrescribe; verify telemetry-arrival where present
LambdaVerify host config (Tier 3) + Prescribe
ECSVerify host config (Tier 3) + Prescribe
EKSVerify telemetry arrival (Tier 1) + Prescribe config (pod env is K8s-RBAC-gated, not IAM-verifiable)
On-prem / multi-cloudPrescribe only

Step 3: Run Checks

Assign each finding a severity (CRITICAL / HIGH / MEDIUM / LOW / INFO) and a type (VERIFY / PRESCRIBE). The complete check catalog with APIs, log-group patterns, and pass/fail logic is in references/checks-catalog.md. Summary below.

3.1 Account prerequisite — CloudWatch Transaction Search (all hosts)

  • VERIFY (Tier 2): xray:GetTraceSegmentDestination → destination must be CloudWatchLogs and status ACTIVE. If XRay/inactive, Transaction Search is not enabled → CRITICAL (spans will not be delivered to CloudWatch Logs; this is the single most common silent root cause).
  • VERIFY (Tier 1 fallback): if the destination API is unavailable, infer from whether span log streams are receiving data (below). Report as inferred, not confirmed.
  • PRESCRIBE: enable Transaction Search and ingest spans as structured logs (see references/remediation-runtime.md).

3.2 Runtime agent path (start here — primary surface)

  1. Agent log group exists (VERIFY, Tier 1): logs:DescribeLogGroups prefix /aws/bedrock-agentcore/runtimes/. Missing → HIGH.
  2. Logs arriving (VERIFY, Tier 1): logs:FilterLogEvents on the runtime log stream in the last 24h. No recent events after invocations → HIGH.
  3. Spans flowing (VERIFY, Tier 1): check the spans log stream in the agent's log group (unified destination) or the shared aws/spans log group. No spans despite invocations → CRITICAL (points to Transaction Search disabled or missing instrumentation).
  4. Session metrics emitting (VERIFY, Tier 1): cloudwatch:ListMetrics namespace bedrock-agentcore. Absent → MEDIUM.
  5. Runtime tracing / span destination (VERIFY, Tier 2): bedrock-agentcore:GetAgentRuntime → inspect env for UNIFIED_TRACES_DESTINATION_ENABLED and DISABLE_ADOT_OBSERVABILITY. Starting 2026-07-20, newly created agents in supported AWS Regions default to the unified span destination (the agent's own log group) — UNIFIED_TRACES_DESTINATION_ENABLED=false is now the opt-out. Agents created before that date remain on shared aws/spans unless opted in. If ADOT observability disabled unintentionally → HIGH.
  6. X-Ray resource policy on log group (VERIFY, Tier 2): logs:DescribeResourcePolicies — must allow xray.amazonaws.com to logs:PutLogEvents on the agent's log group. Applies by default for agents created after 2026-07-20 in supported Regions, since unified span destination is the new default (per the AgentCore release notes). Mark N/A only when the agent was created before 2026-07-20 and still delivers to shared aws/spans, or the customer has explicitly opted out via UNIFIED_TRACES_DESTINATION_ENABLED=false. Missing → HIGH.
  7. Code-level instrumentation (PRESCRIBE): cannot read source. If spans are absent, prescribe: aws-opentelemetry-distro>=0.10.0 (≥0.18.0 for unified span destination) + boto3 in requirements.txt; launch with opentelemetry-instrument python main.py (container CMD ["opentelemetry-instrument","python","main.py"]); framework tracing enabled (e.g. Strands tracer, opentelemetry-instrumentation-langchain); session id via X-Amzn-Bedrock-AgentCore-Runtime-Session-Id.

3.3 Memory & Gateway resources

  • VERIFY (Tier 2): logs:DescribeDeliveries / DescribeDeliverySources / DescribeDeliveryDestinations for an APPLICATION_LOGS source on the resource ARN and a TRACES→XRAY delivery. Default log group /aws/vendedlogs/bedrock-agentcore/{memory|gateway}/APPLICATION_LOGS/{resource-id}. No delivery → HIGH (no logs), MEDIUM (no traces).
  • PRESCRIBE: console log-delivery + tracing toggle, or the put_delivery_source/put_delivery_destination/create_delivery SDK sequence (see references/remediation-memory-gateway.md).

3.4 Built-in tools (code interpreter, browser)

  • PRESCRIBE-first: no service logs by default. Prescribe custom log output + a log destination, and custom headers (X-Amzn-Trace-Id, traceparent) on the tool APIs.
  • VERIFY (Tier 1): if the customer emits logs, confirm the target log group is receiving data.

3.5 Non-runtime hosts

  • Lambda (VERIFY, Tier 3): lambda:GetFunctionConfiguration → OTEL Layer present and AWS_LAMBDA_EXEC_WRAPPER=/opt/otel-instrument; agent log group env vars set. Gaps → HIGH. Note: Lambda uses the Layer, not the aws-opentelemetry-distro package.
  • ECS (VERIFY, Tier 3): ecs:DescribeTaskDefinition → container env carries the OTEL variable set (see 3.6). Gaps → HIGH.
  • EKS (VERIFY telemetry only, Tier 1): confirm the agent log group is receiving data. Pod/ConfigMap env is governed by Kubernetes RBAC and is not verifiable via IAM — prescribe config and report the pod-env checks as prescriptive.
  • On-prem / multi-cloud (PRESCRIBE only): outside DA reach. Emit the full setup.

3.6 Non-runtime OTEL environment (PRESCRIBE / verify where host config is readable)

Required variables (per docs): AGENT_OBSERVABILITY_ENABLED=true, OTEL_PYTHON_DISTRO=aws_distro, OTEL_PYTHON_CONFIGURATOR=aws_configurator, OTEL_RESOURCE_ATTRIBUTES (service.name, aws.log.group.names, cloud.resource_id), OTEL_EXPORTER_OTLP_LOGS_HEADERS, OTEL_EXPORTER_OTLP_TRACES_HEADERS (optional unified spans; needs ADOT ≥0.18.0 + X-Ray log-group resource policy), OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf, OTEL_TRACES_EXPORTER=otlp. Full block with Lambda-specific vars in references/remediation-non-runtime.md. ADOT Collector is not supported — use the ADOT SDK or the Lambda Layer only.

Step 4: Generate the Readiness Report

Produce a shareable artifact named agentcore-observability-review-<target>-<YYYY-MM-DD>.md (<target> = agent/runtime id or a user-supplied label).

Sections:

  • Header — account, region, host, surfaces assessed, permission tier reached, date.
  • Executive summary — ✅ READY / ⚠️ GAPS / ❌ NOT CONFIGURED; finding counts by severity; top 3 items.
  • Findings — table: # | Check | Type (VERIFY/PRESCRIBE) | Severity | Current state | Expected | Remediation.
  • Remediation — concrete, copy-pasteable config/commands per gap (link the relevant references/ file).
  • Permissions to unlock more checks — the exact scoped inline policy JSON for any tier not reached.
  • Limitations — code-level instrumentation is prescriptive; EKS pod env not IAM-verifiable; on-prem/multi-cloud prescribe-only; read-only (customer applies changes).
  • Appendix — reference links.

Severity Definitions

SeverityDefinition
CRITICALObservability is broken — telemetry cannot flow (e.g. Transaction Search disabled, no spans despite invocations).
HIGHSignificant gap — a required piece is missing (log group, delivery, resource policy, host env).
MEDIUMNotable gap — metrics/traces partially configured or best-practice not met.
LOWMinor hardening or optimization.
INFOObservation, no action required.

Reference Files

  • references/checks-catalog.md — full per-check catalog (API, logic, severity, VERIFY/PRESCRIBE).
  • references/iam-tiers.md — the three read-only tiers + ready-to-attach scoped inline policy JSON.
  • references/remediation-runtime.md — Transaction Search, runtime tracing, ADOT, X-Ray resource policy.
  • references/remediation-memory-gateway.md — log delivery + tracing (console + SDK).
  • references/remediation-non-runtime.md — Lambda / ECS / EKS / on-prem OTEL env and setup.

Appendix — Reference Links

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Agentcore Observability Setup AI skill do?

Validates and bootstraps Amazon Bedrock AgentCore observability so customers can trace agent reasoning, detect silent failures, and measure performance before an outage. Use this skill when a user asks to check, validate, audit, verify, fix, or set up AgentCore observability, tracing, metrics, or logging - for example "is AgentCore observability configured correctly", "why can't I see traces for my agent", "my Bedrock agent has no spans in CloudWatch", "validate agent monitoring", "AgentCore observability readiness", or "set up OTEL for my agent". Covers AgentCore Runtime agents, Memory and...

Why use Agentcore Observability Setup on TypingMind?

Because you install it once and use it with any model. Agentcore Observability Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agentcore Observability Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aws/tools-for-devops-agent/tree/main/skills/agentcore-observability-setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Agentcore Observability Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agentcore Observability Setup?

As many as you like. As long as a model supports skills, you can use Agentcore Observability Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agentcore Observability Setup AI skill free?

Yes. It is published on GitHub by aws under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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