How to make Claude background agents 3-5x faster and cheaper
In three regression scenarios, Claude Managed Agents with Causely produced correct fixes 3.6–5.7x faster and at 3.5–5x lower cost than the baseline agent.
Latest
What should trigger an autonomous agent in production?
A control band breach tells an autonomous agent only that a metric moved. A trigger carrying a causal chain tells it what broke and how far the problem reaches.
How to Check an Agent's Diagnosis Before It Touches Production
New Causely MCP tools show which diagnoses an agent ruled out and how far a failure would spread, so teams can verify the agent's reasoning before acting in production.
How to Operate AI Agents in Production: The Three-Layer Stack
Operating AI agents in production takes three layers: instrumentation, evaluation, and causal reasoning, including the root-cause analysis behind agent reliability.
Why A2A Joining the AAIF Matters
Causely joins the AAIF along with A2A from Google. Agents can now talk over an open standard, but what they say is only as good as the context they start from.
How Open-Inspect and Causely close the loop on autonomous remediation
Open-Inspect gives background agents an execution environment. Causely gives them a causal diagnosis. Together, they close the loop from finding to fixing.
Is access enough? Auth patterns for Claude Managed Agents
MCP tunnels let Claude Managed Agents reach and authenticate to real infrastructure, but access alone doesn't make an agent one you'd trust to run on-call.
Introducing Issues, one problem, one thread, start to finish.
On-call means re-triaging the same problem as diagnoses shift underneath you. Causely's new Issues give people and agents one stable thread instead.
Expanded Dynatrace Support: Better, Faster, Cheaper Agents
Causely turns Dynatrace entities, topology, and alerts into a causal model that names the root cause, making on-call agents better, faster, and cheaper.
Your on-call AI agent is wrong more than you think
Most frontier LLMs degrade badly by ~1,000 tokens of input, not the millions in their spec sheets. For on-call agents, that means accuracy drops exactly as an incident gets complex. The fix isn't a bigger model. It's not handing the LLM the raw data at all.
Auto-instrumenting Java with eBPF: No JVM changes required
Getting OpenTelemetry into Java enterprise applications without touching the JVM has been a persistent gap. OBI changes that, and for Causely customers, it unlocks the topology data needed to pinpoint root causes across complex Java microservice architectures.
Causely MCP Skills: stop prompt-engineering your reliability agent
Causely MCP Skills are live. One master router + six specialist workflows: alert triage, change impact, K8s investigation, postmortems & more. Describe your situation, Skills pick the right tools. No prompt engineering. No orchestration.
semantics
Semantics in Observability
Observability semantics fall into six layers, from entity inventory to constraints. Most tooling reaches layer two. This post defines all six precisely.
AI
Why GenAI applications are hard to operate
Standard observability tools were built for deterministic systems. GenAI applications break that contract — token counts shift, tool call patterns change, completion rates drop — and none of it fires an alert. Here is what OTel GenAI instrumentation gives you today, and where the gaps remain.
ebpf
The DNS blind spot hiding in your agentic pipelines
DNS lookup latency is invisible to standard OpenTelemetry instrumentation. eBPF-based tracing closes the gap, and it matters more as agents fan out calls across MCP servers.
AI
A Cursor Plugin for AI Ops Agents You Can Trust in Production
Causely is now a Cursor plugin. Your coding agent gets causal context from your live environment and can move from emerging causes of reliability risk to code-level fixes in the IDE.
Ops agents
Your AI Ops Agent Is Guessing
Named root causes are what turn a guessing agent into one you can trust to act without manual review.
AI
Why AI agents burn tokens on every reliability query
AI agents reconstruct environment state from raw telemetry on every reliability query. Causal context eliminates the reconstruction and cuts token use by 60%.
AI
Why Your AI SRE Agent Is Stuck on Read-Only
Most AI SRE agents are stuck on Read-Only — not because teams lack trust, but because raw telemetry offers no causal context to act on with confidence.
Blog
How Humm Group Delivered Flawlessly in a High-Stakes Product Launch
Launching a new fintech product required certainty across a complex microservices platform. With Causely modeling cause-and-effect relationships across services, Humm Group gained system-level understanding and confidence that critical dependencies behaved correctly during launch.
Reliability Is Managed In Services, But Felt In Transactions
Reliability is managed in services, but users experience outcomes. In complex, multi-service and AI-driven architectures, systems can look healthy in isolation while end-to-end workflows still fail. Product reliability needs visibility at the level of transactions and flows.
Causely product
Alerts Aren’t the Investigation: What Comes Next in Incident Response?
Alerts are signals, not explanations. By explicitly mapping alerts to symptoms and inferred root causes, Causely turns alert noise into a coherent explanation of what is actually happening in the system.
opentelemetry
How to Turn Slow Queries into Actionable Reliability Metrics with OpenTelemetry
Slow SQL queries degrade UX and reliability. This guide shows how to distill OpenTelemetry DB spans into actionable metrics: build span-derived slow-query dashboards, rank queries by traffic impact, and detect regressions with anomaly baselines, so you fix what matters first. Hands-on lab included.
Causely product
When Asynchronous Systems Fail Quietly, Reliability Teams Pay the Price
Causely’s causal model has been expanded for asynchronous messaging systems. Instead of treating queues as opaque buffers, Causely models messaging infrastructure as it operates in production, making asynchronous failures explicit and explainable.