AgentOps: monitor, secure and control the cost of every AI agent.
AgentOps is the discipline of running AI agents reliably in production — tracing what each agent did, evaluating quality, enforcing guardrails, and keeping token spend under control. As companies go from one agent to hundreds, Techcream sets up the AgentOps layer so you can see, trust and budget every agent in one place.
Hundreds of agents need an operations layer
Traditional monitoring tells you a server is up. It can't tell you that an agent looped 400 times overnight, leaked a customer's phone number into a prompt, or quietly got worse after a model update. Agents are non-deterministic, they spend money on every step, and they act on real systems. They need their own operations layer.
50%+
Of organisations say they can't accurately control AI costs
Source: Splunk State of Observability 2026Span-level
Cost tracking is how teams find agent loops that burn budget
Source: MintMCP AgentOps guidePre + post
LLM guardrails: PII redaction and injection checks before, hallucination and action checks after
Source: ArthurOne operations layer for every agent
Every agent run passes through tracing, guardrails and budgets before anyone gets paged.
Your agents
- Support agent
- Sales agent
- Ops agents
- Vendor agents
Instrument
- Traces (OTel)
- Token & cost per step
- Tool calls
Govern
- Guardrails
- Budgets
- Permissions
- Eval gates
Observe
- Quality dashboard
- Spend by team
- Drift alerts
Respond
- Runbooks
- Roll back prompt
- Route to cheaper model
Everything needed to go live.
End-to-end tracing
Every prompt, tool call, retrieval and decision captured per run, per agent, per user.
Cost controls
Token and spend budgets per agent and team, loop detection, and model routing to cheaper models.
Guardrails
PII redaction, prompt-injection detection, output validation and allow-listed actions.
Continuous evaluation
Regression suites and quality scoring that catch drift after prompt or model changes.
Agent registry & access
An inventory of every agent, its owner, permissions, tools and data access.
Alerts & incident playbooks
Alerts for cost spikes, failure rates and guardrail hits, with clear runbooks.
From first call to production.
- 1
Inventory
Find every agent, model, key and tool in use — including shadow AI.
- 2
Instrument
Add tracing and cost attribution with open standards (OpenTelemetry) where possible.
- 3
Govern
Set budgets, guardrails, permissions and evaluation gates.
- 4
Operate
Dashboards, alerts and a monthly review of quality, risk and spend.
Where it pays off first.
- ✓Companies scaling from a few agents to dozens across teams
- ✓Finance teams that need predictable AI spend
- ✓Security teams governing agent access to data and tools
- ✓Product teams shipping AI features that must not regress
- ✓Agencies running agents for many clients
We've built this before.
GrowthAsist
We run LLM features in production in our own product, so quality and cost are our problem too.
MSPAC
Built a secure data-visualisation application handling sensitive data for a US state agency.
Askspot
Multi-tenant AI chatbot platform — isolation and per-client usage were core requirements.
Why teams choose Techcream for AgentOps.
Tool-agnostic
We work with Langfuse, Helicone, Datadog, Arize, Braintrust or custom stacks — whatever fits.
Builders, not auditors
We can fix what monitoring finds, because we build agents too.
Cost-obsessed
Model routing, caching and prompt optimisation usually pay for the AgentOps work itself.
Security-minded
Least-privilege tools, secrets hygiene and audit trails come standard.
AgentOps, explained.
AgentOps is the operational practice for AI agents in production: observability (tracing what agents did), evaluation, guardrails, security and cost management — similar to DevOps or MLOps, but for autonomous agents.
LLMOps focuses on models and prompts. AgentOps adds the realities of agents: multi-step runs, tool calls with real-world side effects, loops, and permissions across many agents and teams.
By attributing cost to each agent and step, detecting loops, routing simple steps to smaller models, caching repeated work, trimming prompts, and enforcing budgets with alerts.
No. We recommend tools based on your stack and scale, and use open standards like OpenTelemetry so you're not locked in.
Let's scope your AgentOps project.
Tell us about the workflow or problem. We'll reply within 24 hours with initial thoughts and a time to talk — no sales pitch.
Related services