Observability

How to see what your episodes are doing: log strategies, the synthesis storage pipeline, telemetry, the step journal, plus the declarative controls for sampling, service levels, and adaptive budgets.

Examples use a generic resource domain.

Log strategies

Set per-expectation via log_strategy. Controls both what gets stored in step journal columns (args_redacted, result_ref) and what the materialized log renders:

Use :full_debug for audit-sensitive workflows where you need to reconstruct exactly what context an LLM had. Use :timeline for high-frequency episodes where you want the flow visible without storing full payloads.

Important: log_strategy is the last layer in a pipeline. By the time it runs, earlier layers have already trimmed the data. "Full" in :full_debug means "everything that survived the earlier layers", not necessarily everything the strategy originally passed. See the pipeline below.

The materialized log is built by LogProjector reading back from the already-stored step rows — it never re-processes the original payload. Whatever log_strategy stored is exactly what appears in the log.

Storage pipeline for synthesis steps

Every synthesis step passes through three filtering layers in order before anything reaches the database:

synthesis payload from next_step
        │
        ▼
1. __transient__ stripping   — keys listed in :__transient__ are removed from storage
        │                       but the synthesizer receives the full payload
        ▼
2. tool redact callbacks     — redact/1 and redact_result/1 on tool steps
        │                       (not applicable to synthesis, but runs for tool_call steps)
        ▼
3. log_strategy filtering    — controls final shape of args_redacted / result_ref
        │
        ▼
   cyclium_episode_steps (args_redacted, result_ref)
        │
        ▼
   cyclium_episode_logs (rendered by LogProjector from stored step rows)

Each layer has a different scope:

Transient synthesis data

Sometimes a strategy needs to pass large data to the synthesizer (full record lists, raw API payloads) that the LLM needs for context but that you don't want persisted in the step journal or materialized log. Mark those keys under :__transient__ in the synthesis payload:

def next_step(state, _ctx) do
  records = load_records(state.resource_id)

  {:synthesize, %{
    resource_id: state.resource_id,
    resource_name: state.resource_name,
    record_count: length(records),         # small scalar — kept in storage
    records: serialize_records(records),   # full list — synthesizer needs it, storage does not
    evidence: build_evidence(records),     # small structured summary — kept in storage
    __transient__: [:records]              # strip :records before writing args_redacted
  }}
end

The runner passes the full map (minus :__transient__ itself) to synthesizer.synthesize/2, then drops the listed keys before handing off to log_strategy filtering. The synthesizer receives records; the stored step and rendered log do not, regardless of log_strategy.

This is the right tool when:

• You need full detail for synthesis quality (long record lists, raw API responses, document text)
• You don't want that data in the audit trail or materialized log
• You still want other context fields (counts, summaries, IDs) persisted for debugging

It is not a substitute for log_strategy — if you want no storage at all for an episode type, use :none or :summary_only. __transient__ is surgical; log_strategy is wholesale.

Materialized logs are stored in cyclium_episode_logs by Cyclium.LogProjector and can be queried via Cyclium.Episodes.get_log(episode_id).

Telemetry

Cyclium emits 36 structured telemetry events under the [:cyclium, ...] prefix. Attach a handler for development:

Cyclium.Telemetry.attach_default_logger()

Key events:

Full list: Cyclium.Telemetry.events/0. Work-claim telemetry is documented in the Distributed Ops guide.

Step journal

Every episode action is recorded as an EpisodeStep with one of 16 kinds:

tool_call, synthesis, observation, checkpoint, output_proposed, output_delivered, output_failed, approval_requested, approval_resolved, wait_started, wait_resolved, finding_raised, finding_updated, finding_cleared, episode_completed, episode_failed

Each step records: step_no, kind, tool_name, args_redacted, result_ref, error_class, error_detail, cost_tokens, cost_ms, created_at.

Query steps: Cyclium.Episodes.list_steps(episode_id)

Episode sampling

Probabilistic episode firing for high-frequency triggers. Set sample_rate on an expectation to control what fraction of triggers actually fire episodes:

expectation(:check_metrics,
  strategy: MyApp.Strategies.MetricsCheck,
  trigger: {:event, "metrics.updated"},
  sample_rate: 0.1  # fire ~10% of triggers
)

• nil or 1.0 = always fire (default)
• 0.0 = never fire
• Sampled-out episodes emit [:cyclium, :episode, :sampled_out] telemetry
• Force-fired episodes bypass sampling

Service level tracking

Declarative performance objectives with automatic breach detection. Define success rate and duration thresholds per expectation:

expectation(:process_request,
  strategy: MyApp.Strategies.RequestProcessor,
  trigger: {:event, "request.created"},
  service_levels: %{
    max_duration_ms: 30_000,  # p95 target
    success_rate: 0.95,       # 95% success target
    window_episodes: 20       # rolling window size
  }
)

Breaches emit [:cyclium, :service_levels, :breach] telemetry and a "service_levels.breach" Bus event with details:

%{type: :success_rate, current: 0.85, threshold: 0.95}
%{type: :duration, current: 45_000, threshold: 30_000}

Query metrics: Cyclium.ServiceLevels.metrics(actor_id, expectation_id) returns %{success_rate: f, p95_duration_ms: n, sample_count: n}.

Adaptive budgets

Advisory budget tracking based on historical episode resource usage. When enabled, Cyclium records turns, tokens, and wall time for each completed episode and recommends budgets based on p95 values with 25% headroom.

expectation(:classify_item,
  strategy: MyApp.Strategies.ItemClassifier,
  trigger: {:event, "item.created"},
  adaptive_budget: true
)

Query recommendations:

# After enough samples (minimum 5):
Cyclium.AdaptiveBudget.recommend(actor_id, expectation_id)
# => %{max_turns: 8, max_tokens: 15_000, max_wall_ms: 25_000}

# Detailed stats:
Cyclium.AdaptiveBudget.stats(actor_id, expectation_id)
# => %{samples: 47, p50: %{...}, p95: %{...}, max: %{...}}

Adaptive budgets are advisory only — they do not automatically adjust episode budgets. Use the recommendations to tune your expectation configs over time.

Related guides: Actors & Strategies · Findings & Outputs · Distributed Ops