Observability bolted onto agents after the fact creates gaps and adds complexity. Building it into the runtime from the start produces fundamentally better visibility with less effort. See built-in observability in action →
When an agent does something wrong in production, the question is never whether you need to understand what happened. The question is whether you can. Most agent deployments treat observability as an add-on - something to integrate later, through a separate product, with additional configuration. This approach creates gaps, adds complexity, and often fails to capture what you actually need when problems occur. Building observability into the runtime from the start produces fundamentally better visibility with less effort.
The Visibility Problem
Agents are opaque by default. A user sends a message. The agent responds. Between those two events, the agent might have reasoned through multiple approaches, called several tools, received and processed various results, updated its memory, and made numerous decisions. All of this internal activity is invisible unless you explicitly capture it.
Without visibility, debugging becomes speculation. A user reports that the agent sent an email to the wrong person. Your logs show an email was sent. But why that recipient? What information did the agent have? What alternatives did it consider? Without the internal reasoning and decision chain, you are guessing.
The challenge compounds when problems are subtle. An agent that occasionally gives poor answers is harder to diagnose than one that crashes. The agent completes its task, so there is no error to catch. Finding the issue requires examining the agent's reasoning for cases that look similar but differ in quality. This requires observability that you probably did not know you needed when the agent was deployed.
The pattern is familiar from other domains. You do not know which logs and metrics you need until you have an incident. By then, it is too late to add them. Agent observability follows the same pattern with even higher stakes because agent behavior is inherently more complex and variable than traditional software.
The Add-On Approach and Its Limitations
Most agent frameworks treat observability as an integration. You add a tracing library, configure credentials for an external service, enable the appropriate flags. Your agent runs now send traces to a separate dashboard where you can review them.
This works, technically. But the add-on approach has structural limitations.
Configuration is required. Someone must remember to enable tracing, configure it correctly, and maintain the configuration over time. Incorrect configuration means missing data, discovered only when you need the data and find it absent.
External dependency is introduced. The observability service is now part of your agent's dependency chain. If it is unavailable, does your agent still run? Does it fail? Does it silently drop traces?
Completeness depends on instrumentation. The tracing SDK captures what it instruments. Custom tools, non-standard patterns, and agent-specific behaviors might not be captured unless you add custom instrumentation.
Context is split across systems. Conversation history is in one place, traces in another, logs somewhere else. Reconstructing the complete picture requires correlating across systems, usually manually.
Additional cost accumulates. Per-trace pricing for observability services adds up quickly for high-volume agent usage. The cost of visibility becomes another line item to manage.
These limitations are not specific to any particular observability product. They arise from the architectural choice to treat observability as an external addition rather than an intrinsic capability.
inference.sh has observability built in. Every agent run is traced automatically. No configuration, no external products, no additional cost. Learn more →
The Built-In Alternative
When observability is part of the runtime itself, the limitations of the add-on approach disappear.
No configuration required. Every agent run is observed automatically. There is nothing to enable, nothing to configure incorrectly, no setup to forget. Visibility is the default state.
No external dependency. Observability data captures alongside execution in the same infrastructure. Availability is not a separate concern.
Completeness is architectural. Because the runtime executes the agent, it sees everything the agent does. Tool calls, reasoning steps, state changes - all captured at the execution layer, not through SDK instrumentation.
Context is unified. Conversation history, execution traces, and agent state live in the same system. No correlation required. Looking at a conversation shows the traces. Looking at a trace shows the context.
No additional cost. Visibility comes with the runtime. You pay for execution, not separately for observation.
This architectural difference matters most during incidents. When something goes wrong and you need to understand what happened, built-in observability means the data is there, complete, in one place, without extra steps.
What Should Be Captured
Comprehensive agent observability captures several categories of information.
Conversation flow includes every message exchanged between user and agent, with timestamps. User messages, agent responses, and any system messages are all part of the conversation. This is the context within which agent behavior makes sense.
Reasoning traces capture the agent's internal decision process. When the agent considers what to do next, that reasoning should be visible. This is crucial for understanding why the agent made particular choices.
Tool calls include the tool name, input parameters, output results, duration, and any errors. Knowing what information the agent requested and received explains how it reached its conclusions.
State changes track updates to agent memory, plan progress, and other persistent state. If the agent stores something for later use or marks a task step complete, that transition should be recorded.
Sub-agent interactions include delegations to other agents and their results. In multi-agent systems, understanding the complete picture requires visibility across the agent hierarchy.
Timing information shows how long each operation took. Slow steps become visible, enabling performance optimization and explaining user-perceived latency.
Token usage tracks consumption at each step. Understanding cost requires knowing where tokens are spent.
Together, these categories provide a complete picture of agent behavior - not just what it did, but why it did it, how long it took, and what information it was working with.
Using Observability Data
Having comprehensive observability data enables several activities beyond basic debugging.
Incident investigation becomes faster and more reliable. When a user reports a problem, you can pull up the exact conversation, see the complete reasoning chain, examine every tool call and result, and identify precisely where things went wrong.
Performance optimization uses timing data to identify bottlenecks. If a particular tool is slow, it shows in the traces. If the model is taking too long for certain types of requests, patterns emerge from the data.
Cost analysis breaks down where resources are consumed. Token usage per conversation, tool execution costs, and time spent across different activities all become visible. This enables informed decisions about optimization and pricing.
Quality monitoring tracks patterns in agent behavior over time. Are certain types of requests handled well? Are failure rates changing? Are there emerging patterns in how agents struggle?
Compliance and audit requirements are met when every agent action is recorded with timestamps and context. Who approved what, when, and based on what information - all visible in the observability data.
Agent improvement uses observability patterns to inform prompt refinement and tool design. If agents frequently struggle with certain types of requests, the data reveals what they are working with and where they go wrong.
The value of observability data grows over time as you accumulate history. Patterns that are invisible in single conversations become clear across thousands.
Observability Architecture
For built-in observability to work well, the architecture must capture data at the right points without impacting performance.
The runtime captures events as they occur during agent execution. Each event is a structured record with standardized fields for type, timestamp, and relevant data. These events stream to storage as they happen, not buffered until the end of execution.
Storage must handle high write volume with eventual queryability. Observability data is append-only - events are never modified after capture. This enables efficient write patterns and simpler storage architecture.
Query patterns include both point lookups (show me this conversation) and analytical queries (what percentage of conversations had tool failures this week). The storage and query architecture must support both.
Retention policies determine how long data is kept. Compliance requirements may dictate minimums. Storage costs may impose practical maximums. The right policy depends on your use case and constraints.
Access control ensures that observability data is visible to those who need it and protected from those who should not see it. User conversation data is sensitive. Appropriate controls and audit trails are necessary.
For teams building agents, inference.sh includes observability as a core runtime capability. Every agent run captures the complete trace automatically. The same workspace where you develop and deploy agents provides visibility into how they behave. No separate products, no additional configuration, no integration work.
Observability is not a feature you add when you have time. It is a capability you need from the start. Building it into the runtime ensures it is always there when you need it.
FAQ
How does built-in observability compare to using a dedicated observability product?
Dedicated observability products offer powerful querying, visualization, and alerting capabilities built up over years of development. Built-in observability trades some of that sophistication for completeness and integration. You get every event automatically without instrumentation effort, unified context without correlation across systems, and no additional cost or configuration. For agent-specific visibility - understanding reasoning chains, tool call patterns, and conversation flow - built-in observability often provides better data because it captures at the execution layer. For broad monitoring across many systems, dedicated products may still have a role. The approaches can complement each other when needed.
What happens to observability data at high volume?
High-volume agent usage generates substantial observability data. Built-in observability must handle this through efficient storage architecture and sensible retention policies. Append-only event streams write efficiently. Older data can be summarized or aged out based on retention policy. Query patterns determine indexing strategy. Most implementations can handle thousands of conversations per second with appropriate infrastructure. If you are concerned about volume, check specific limits and discuss retention options. The key is that the architecture plans for scale from the start rather than struggling when volume grows.
Can I export observability data to other systems?
Most built-in observability implementations provide export capabilities - APIs to query events, webhooks for real-time event streaming, or batch export for analytical processing. The specific options vary by platform. If you need to correlate agent observability with other systems - application logs, infrastructure metrics, business analytics - export capabilities enable that integration. Check the specific platform documentation for available options and formats.