Skip to main content
CodeDTX
Enterprise AI

AI Agent Observability and Tracing

What does AI agent observability involve?

Direct answer

Agent observability records each run as a trace: the tools called, the evidence retrieved, the decision reached, and the cost. On top of that sit the signals that matter operationally - refusal rate, approval rate, input freshness and cost per run - because agents degrade quietly rather than failing loudly.

Definition

AI agent observability

The instrumentation that makes an agent's behaviour visible: a trace per run covering tool calls, retrieved evidence and outcome, plus aggregate signals that reveal degradation before a person reports it.

Scope

The signals worth alerting on

Traces explain a single run. These aggregates tell you something changed.

  • Refusal rate

    A rise usually means an interface or the input data changed underneath the agent.

  • Approval rate with sampling

    Rate alone hides rubber-stamping, so read a sample of approved items in full.

  • Rejection reasons

    Clustering on one theme names the next thing to fix, and each is a free eval case.

  • Input freshness

    A stopped sync is silent; the agent keeps producing confident output from stale data.

  • Cost per run

    A quiet climb usually means retries or growing context.

  • Version in force

    Which model and prompt version each run used, so behaviour ties to a release.

Scope

Why degradation is the hard case

A crashed service pages someone. An agent whose proposals have become slightly worse produces no error at all, and the first report comes from a frustrated reviewer weeks later. That is why the aggregate signals matter more here than in conventional services, and why they need a named owner rather than a dashboard nobody opens.

Named methodology

The Propose–Decide–Execute pattern

CodeDTX builds agentic systems on the Propose–Decide–Execute pattern: agents may only write proposals with evidence attached, a named human records an approval or rejection with a reason, and a separate execution layer carries out approved work and logs the artifact. No agent holds a write tool to the outside world.

  1. 01

    Propose

    The agent analyses live system state and drafts a change, with the evidence it relied on attached to the proposal.

  2. 02

    Decide

    A named human approves, edits, or rejects with a reason. Risk tier determines who is allowed to decide.

  3. 03

    Execute

    A separate execution layer performs approved work - merge, publish, call, write - and records the resulting artifact.

  4. 04

    Audit

    Actor, reason, evidence, artifact, tokens, and cost are retained for every run, so any decision can be reconstructed later.

Reference architecture

The six layers we build and review against

  1. 1

    Agent layer

    Agent architecture, tool use, memory and context, multi-agent patterns, structured outputs, orchestration.

  2. 2

    Integration layer

    MCP servers, tool contracts, API and database adapters, authentication, permissions, legacy system access.

  3. 3

    Knowledge layer

    Retrieval and RAG, vector and search architecture, enterprise knowledge sources, data access controls.

  4. 4

    Reliability layer

    Evals, tracing, observability, cost and latency budgets, fallbacks, regression tests.

  5. 5

    Safety layer

    Guardrails, prompt-injection defense, PII and data boundaries, human-in-the-loop gates, audit trails.

  6. 6

    Product layer

    The application people actually use: interfaces, approval queues, and operational runbooks.

Questions

Frequently asked

Have a workflow that should become AI-enabled?

Tell us about the system it lives in. We reply from an engineering seat, not a sales deck.

Talk to our engineers