Managed LLM-Ops & AI Agent SRE

Managed AI agent services deliver ongoing production operations for AI agents and multi-agent systems — 24/7 observability (LangSmith / Langfuse / Arize), prompt versioning & A/B testing, drift & hallucination monitoring, quarterly LLM upgrades with regression gates, guardrail tuning, integration health monitoring, eval-set expansion, cost optimisation and SLA-backed incident response. Three tiers — Bronze (business hours), Silver (extended) and Gold (24/7 with named SRE).

Three reasons: (1) Cost — a competent in-house LLM-ops + SRE team runs $1.2M–$2M fully-loaded annually (3–6 senior engineers). Managed services start at $60K / year (Bronze) and cap at $300K / year (Gold). (2) Specialisation — DreamzTech runs 100+ production agents; your in-house team will spend 6–12 months catching up. (3) Continuity — turnover risk is on us. Best for enterprises with 1–10 production agents; building in-house starts making sense at 15+.

Bronze ($5K/mo): business-hours support, 1 agent system, monthly eval and review, quarterly model upgrade, LangSmith observability, Slack channel, P1 response within 4 business hours. Silver ($12K/mo): 16/5 support, up to 3 agents, weekly eval and bi-weekly review, full observability stack, PagerDuty, P1 within 1 hour. Gold ($25K/mo): 24/7 named SRE, unlimited agents, weekly eval and review, cost optimisation, integration drift monitoring, P1 within 15 minutes, named incident commander.

LangSmith for LLM call tracing and prompt versioning. Langfuse for open-source self-hosted observability. Arize for embedding drift and outcome distribution monitoring. Datadog / Grafana / Prometheus / OpenTelemetry for infrastructure observability. Sentry for application errors. PagerDuty / Opsgenie for SLA-backed incident routing. We compose these per client based on cloud (AWS / Azure / GCP) and compliance needs.

Three-stage process: (1) Shadow-mode evaluation against ground-truth dataset for 1–2 weeks before live cutover; (2) Side-by-side regression evals — every prompt run through old and new model, accuracy / latency / cost compared; (3) Canary rollout — 1% → 10% → 50% → 100% traffic over 1–2 weeks with auto-rollback on SLO breach. Common upgrades: GPT-4o → GPT-5, Claude 3.5 Sonnet → Claude 4, Llama 3.1 → Llama 3.3. Zero regressions across 100+ managed deployments since 2024.

Five layers: (1) Embedding-distribution drift on every prompt/response pair vs baseline; (2) Hallucination scoring via reviewer-LLM cross-checking citations; (3) Faithfulness metrics for RAG agents (Ragas, TruLens); (4) Outcome distribution drift — track final agent answers / tool calls vs historical; (5) User feedback signal — thumbs-up/down, escalation rates. Threshold breaches trigger Slack / PagerDuty alerts with severity routed by tier.

Starts at $5,000 / month Bronze (business hours, 1 agent, monthly review). $12,000 / month Silver (16/5, up to 3 agents, weekly eval, PagerDuty). $25,000 / month Gold (24/7 named SRE, unlimited agents, weekly review, cost optimisation). Custom Enterprise tier for 10+ agents, regulated industries (FedRAMP / IL5 / HIPAA-covered) or named-author EEAT requirements — typically $40K–$80K / month with dedicated team.

Continuous monitoring across every connected system — Salesforce, ServiceNow, SAP, Oracle, Microsoft Dynamics 365, NetSuite, Workday, HubSpot. Four signal types: (1) API version drift — daily diff against pinned schemas; (2) Breaking-change advisories from each vendor’s release notes; (3) OAuth token health — rotation status, refresh failures; (4) Rate-limit utilisation with predictive alerts at 70% / 85% / 95% thresholds. Pre-emptive adapter updates before vendor-side breakage.

Five techniques: (1) Per-task model routing — Claude 3.5 for nuanced reasoning, GPT-4o for code, Llama 3.3 for high-volume classification; (2) Prompt caching on Anthropic and AWS Bedrock for repeated system prompts; (3) Response caching for deterministic queries; (4) Fine-tuned smaller models replacing frontier-model calls for narrow agents; (5) Batched inference for high-volume offline workloads. Typical results: 40–70% LLM cost reduction without accuracy loss across managed deployments.

Bronze SLA: P1 within 4 business hours, monthly SLO report, 99% target uptime. Silver SLA: P1 within 1 hour, weekly SLO report, 99.5% target uptime, named technical lead. Gold SLA: P1 within 15 minutes, weekly SLO report, 99.9% target uptime, named on-call SRE, named incident commander, monthly executive review. SLAs are contractual with credit clauses on breach.

Typical onboarding takes 3–4 weeks: (1) week 1 — operational readiness assessment, observability stack installation, runbook documentation; (2) week 2 — eval harness setup, ground-truth dataset import, initial baseline established; (3) week 3 — drift detection calibration, incident response playbook validation; (4) week 4 — first scheduled review and SLA activation. Emergency onboarding (P1 production agent in crisis) can compress to 5–7 days.

Yes. We manage AI agents on AWS, Azure, Google Cloud and on-premise / hybrid configurations including AWS GovCloud, Azure Government and Google Cloud Public Sector. Cross-cloud agents (Bedrock + Vertex + Azure OpenAI in one workflow) are supported. Air-gapped managed operations available for defense, intelligence and regulated finance clients via self-hosted observability and offline eval pipelines.

When PagerDuty triggers, our on-call SRE acknowledges within SLA (15 min Gold / 1 hr Silver / 4 business hr Bronze). Runbook-driven mitigation first (rollback prompt, switch model, throttle traffic). Status page updated, incident commander named for Gold tier. Customer comms within 30 min for P1. Post-incident review within 48 hours with named root cause, action items and prevention plan. Monthly executive review aggregates incidents.

Yes — most common engagement model. We typically own LLM-ops disciplines (observability, eval, drift, model upgrades, integration health, cost) while your team owns business logic, prompts and product features. Joint runbooks, shared on-call rotations on Gold tier, weekly handoff syncs and quarterly architecture reviews. Many clients use us as accelerator while ramping their own LLM-ops practice.

Every prompt, response, tool call, retry, fallback and human approval logged with immutable trails (CloudTrail, Azure Monitor, Sentinel, SIEM). Replay-ready evidence packets generated on demand for auditors. SOC 2 Type II controls reviewed annually with evidence delivered to your audit team. NIST AI RMF documentation maintained monthly. EU AI Act conformity assessment records for high-risk classifications. Zero blocking audit findings across managed engagements.

Emergency engagement available. Skip the standard 3–4 week onboarding — within 24 hours we can deploy LangSmith / Langfuse observability, run an emergency triage assessment, identify root cause and stabilise the agent. Gold-tier-equivalent support during the emergency. Full onboarding catches up in weeks 2–4 after stabilisation. Common emergency scenarios: model upgrade regression, integration breakage after CRM release, sudden accuracy drop after prompt change.

Prompts are Git-versioned with semantic versions (v1.4.2). Every change goes through: (1) local eval against ground-truth dataset; (2) shadow-mode A/B in production (old prompt for users, new prompt evaluated in parallel); (3) canary rollout — 1% → 10% → 50% → 100% over 3–7 days with auto-rollback on SLO breach. Rollback is one-click via LangSmith or PromptLayer. Prompt change history fully audited.

Yes — and that’s our default architecture. Per-task model routing lets us use Claude 3.5 for nuanced reasoning, GPT-4o for code generation, Llama 3.3 70B for cost-sensitive high-volume tasks, and Gemini 2.0 for low-latency / long-context. Managed services keep all vendor relationships healthy, manage per-vendor rate limits and outages, and re-route automatically when one vendor degrades. Single pane of glass observability across all vendors.

Yes — for agents producing publishable content (technical articles, financial analyses, legal briefs). Every output is tagged with the producing agent identity, the human reviewer identity, the review timestamp and a content provenance chain. Critical for SEO EEAT signals and regulatory environments (financial-promotion rules, medical-advice compliance). Available on Silver and Gold tiers.

Multi-vendor architecture is the first line of defence — agents automatically failover to backup models when primary vendor degrades. Monitoring includes vendor status pages, latency / error-rate anomaly detection and automatic traffic re-routing. For Gold-tier clients, post-incident reviews include vendor-outage timelines and recommended diversification strategies. Most clients running multi-vendor architectures see zero user-facing impact from major vendor outages.

Six metrics tracked monthly: (1) agent uptime vs SLA target; (2) LLM cost per task trend; (3) accuracy / faithfulness / hallucination rate; (4) incident count by severity; (5) integration health uptime; (6) cost avoidance vs in-house team alternative. Monthly executive review compares against baseline. Typical Gold-tier ROI: $300K managed cost prevents $1.2M+ in-house cost and $400K+ LLM spend through optimisation = 5–6× return.

Yes. Standard contract is month-to-month with 30-day notice. Tier changes (upgrade or downgrade) take effect on the next billing cycle. Discounted annual contracts available (10–15% off). No cancellation fees. Mid-contract we may suggest tier changes ourselves — Bronze clients ramping to 3+ agents typically need Silver; Silver clients hitting 24/7 customer-facing workloads typically need Gold.

Four phases — the DreamzTech OPERATE Framework: Onboard (operational readiness assessment, observability install, baseline); Operate (24/7 monitoring, alerting, incident response per SLA tier); Evaluate (weekly evals, monthly drift reports, quarterly LLM upgrades); Refine & Report (continuous optimisation, monthly executive reviews, quarterly architecture reviews, annual SOC 2 / NIST AI RMF documentation refresh).

Book a free 30-minute managed-services architect call. Bring your production AI agent footprint (number of agents, daily volume, integrations, current observability, regulatory needs) and a senior LLM-ops architect will recommend a tier (Bronze / Silver / Gold), observability stack, eval suite and SLA structure. Then we send a written proposal within 1 business day with fixed monthly rate, onboarding plan and named SRE assignment. No sales pitch, no obligation.