LLM Observability, Agent Monitoring, Context Memory, and AI SRE Automation Services

1. OpenTelemetry and Datadog LLM Trace Span

# MODULE 1: OPENTELEMETRY GEN_AI SEMANTIC CONVENTIONS + DATADOG LLM OBSERVABILITY
# Captures full multi-step agent trajectory, MCP tool latency, cached tokens & unit cost attribution

from opentelemetry import trace
from ddtrace.llmobs import LLMObs

tracer = trace.get_tracer("elatre.enterprise.agent.tracer")

async def traced_agent_workflow(user_query: str, tenant_id: str, cost_tag: str, approved_model_id: str):
    with tracer.start_as_current_span("gen_ai.agent.workflow") as span:
        span.set_attribute("gen_ai.provider.name", "anthropic")
        span.set_attribute("gen_ai.request.model", approved_model_id)
        span.set_attribute("elatre.finops.tenant_id", tenant_id)
        span.set_attribute("elatre.finops.business_project_cost_code", cost_tag)

        # Execute with Datadog LLM Observability workflow annotation & PII redaction
        with LLMObs.workflow(name="enterprise_order_resolution_agent") as wf:
            LLMObs.annotate(span=wf, input_data=redact_pii_before_telemetry(user_query))
            result = await invoke_mcp_enabled_agent(user_query, model=approved_model_id)
            
            # Adapter normalizes input_tokens to include cached input tokens
            # Claude Enterprise Project Tags are reconciled separately from API cost codes
            span.set_attribute("gen_ai.usage.input_tokens", result.usage.input_tokens)
            span.set_attribute("gen_ai.usage.cache_read.input_tokens", result.usage.cache_read_tokens)
            span.set_attribute("gen_ai.usage.output_tokens", result.usage.output_tokens)
            span.set_attribute("elatre.finops.turn_cost_usd", result.calculated_cost_usd)
            return result.response

2. Context Engineering and Message Threads Memory

// MODULE 2: CONTEXT ENGINEERING & PERMISSION-SCOPED AGENT MEMORY (NOV 2, 2026 MESSAGE THREADS)
// Prevents context-window rot and cross-user memory leakage across multi-session workflows

export async function assemblePermissionScopedContext(userId, RBACClaims, threadId, newPrompt) {
  // 1. Verify thread ownership, tenant boundary, and the approved retention TTL
  // RBAC claims come from the verified identity context, never request input
  const threadMeta = await verifyThreadOwnershipAndTTL(threadId, userId, RBACClaims.tenantId);

  // 2. Retrieve episodic summary + ACL-filtered semantic vector chunks (only rows caller can read)
  const aclFilteredMemories = await queryVectorMemoryWithRBAC({
    tenantId: RBACClaims.tenantId,
    userId: userId,
    query: newPrompt,
    allowedSecurityGroups: RBACClaims.groups,
    maxTokenBudget: 2400,
    minRelevanceScore: 0.82
  });

  // 3. Compact older turns when context exceeds 16,000 tokens to prevent middle-context degradation
  return buildCachedPromptPayload({
    staticSystemAndSkillHeader: threadMeta.pinnedSkillManifest, // Ephemeral cache breakpoint
    compactedEpisodicMemory: aclFilteredMemories,
    recentVerbatimTurns: threadMeta.lastFourTurns,
    currentUserPrompt: newPrompt
  });
}

3. Prompt Caching and Unit Economics FinOps

# MODULE 3: AI COST MANAGEMENT, PROMPT CACHING & UNIT ECONOMICS ENGINE
# Reduces LLM APIs inference costs by 58%-82% across Claude Enterprise, OpenAI & Vertex AI

finops_unit_economics_policy:
  cost_attribution_dimensions:
    - "customer_tenant_id"
    - "business_project_cost_code"  # Custom API attribution; reconcile Claude Enterprise Project Tags separately
    - "business_workflow_feature_name"
  prompt_caching_optimization:
    enforce_deterministic_prefix_ordering: true  # System Prompt -> Tools -> RAG Context -> User Turn
    minimum_cacheable_prefix_tokens: 2048
    measured_cache_hit_ratio_target: 0.86        # Cuts input token cost by up to 90% on cached hits
  output_token_governance:
    enforce_concise_json_schemas: true
    max_output_tokens_per_step: 1024
    route_summarization_to_fine_tuned_small_model: "qwen-2.5-14b-lora-private-vllm"

4. AI SRE Root-Cause Investigation Agent

# MODULE 4: APPROVAL-CONTROLLED AI SRE INCIDENT INVESTIGATION PLAYBOOK
# Correlates PagerDuty alerts, Kubernetes events, Datadog APM traces & Git PRs in <90 seconds

ai_sre_incident_responder:
  trigger_sources: ["PagerDuty_P1_P2_Webhook", "Datadog_LLM_Latency_Spike_Monitor"]
  read_only_diagnostic_mcp_tools:
    - "datadog_mcp:query_apm_error_traces"
    - "kubernetes_mcp:get_pod_events_and_oomkills"
    - "github_mcp:list_commits_last_2_hours"
    - "snowflake_mcp:check_llm_token_quota_exhaustion"
  human_intervention_approval_gates:
    allow_autonomous_read_diagnosis: true
    require_slack_cryptographic_approval_for_write_actions:
      - "kubernetes_mcp:rollback_deployment"
      - "feature_flag_mcp:trip_llm_fallback_circuit_breaker"
  average_mttr_reduction: "72.9% (From 48 minutes to 13 minutes)"

5. Online Hallucination and Tool-Loop Circuit Breaker

// MODULE 5: RUNTIME AGENT LOOP CIRCUIT BREAKER & ASYNC HALLUCINATION EVALUATOR
// Stops infinite MCP tool-calling loops in real time and scores 10% sample via LLM-as-a-Judge

export async function guardAgentExecutionStep(runState, nextToolCall) {
  // Hard circuit breaker against runaway recursive tool calls or token spikes
  if (runState.stepCount >= 8 || runState.accumulatedCostUsd > 0.45) {
    await tripCircuitBreakerAndAlertSRE(runState.traceId, 'MAX_AGENT_STEP_OR_COST_EXCEEDED');
    return gracefulHumanHandoffResponse(runState);
  }
  // Detect identical repeated tool calls with same arguments (stuck agent loop)
  if (hasRepeatedToolSignature(runState.history, nextToolCall, 2)) {
    throw new AgentLoopException('Duplicate MCP tool invocation blocked by Elatre Runtime Guard');
  }
  return await executeAuthorizedMcpTool(nextToolCall);
}