Technical field guides for production AI
Build systems you can explain, secure, and operate.
Signal Studio turns difficult engineering questions into direct answers, diagnostic methods, and reusable implementation patterns.
Start with the problem
What are you trying to fix?
01
My incident agent cannot prove root cause.
Compare evidence, alternatives, and observation gaps before proposing a production change.
Open guide 02I need to observe an AI agent.
Trace models, retrieval, tools, memory, delegation, state, and confirmed effects.
Open guide 03I need to control AI cost.
Measure cost per useful outcome, then route, cache, and constrain deliberately.
Open guide 04I need to secure an AI agent.
Control prompt injection, tool permissions, identity, sandboxing, memory, and audit trails.
Open guideTechnical domains
Explore the system behind the symptom.
Use a topic hub for the broad problem, then move to the guide that owns your specific decision or failure mode.
AIOps alerting and operationsEvaluate alert quality, grouping, forecasting, diagnosis, and bounded recovery around real operations decisions.LLM judge evaluationDesign task-specific rubrics, expert reference sets, error measures, stress tests, and calibration records for model judges.Evolving semantic layers for data agentsGovern metric definitions, provenance, retrieval, semantic changes, and drift in data-agent workflows.Enterprise observability platform selectionCompare platform architecture, investigation coverage, operating cost, proof-of-concept evidence, and migration readiness.ClickHouse observabilityPlan ClickHouse-backed telemetry around ingestion durability, signal meaning, investigative queries, capacity, and platform health.Ontology-driven enterprise deliveryConnect business decisions to governed meaning, source mappings, operational authority, and reusable product capability.Build your first app with AIFollow a beginner path from a readable web page to a small app with checkable changes, recoverable data, and a deliberate release.MCP in productionOperating Model Context Protocol services through accountable boundaries, catalog identity, service objectives, compatibility, admission, and remote dependency control.Agent skills in productionReusable agent capabilities with focused discovery, deliberate authoring, immutable releases, coexistence evidence, and governed improvement.AI agent platform engineeringProduction deployment, release compatibility, control-plane resilience, worker lifecycle, tenant scheduling, and admission for agent services.AI agents for SRE and root cause analysisEvidence-aware incident investigation across traces, logs, metrics, topology, and operational change.AI cost engineeringOutcome units, request ledgers, model routing, caching, capacity, and the cost of failed attempts.Context engineering, memory, and stateProduction context assembly, memory governance, durable workflow state, provenance, and side-effect safety.Engineering judgment with AIProblem framing, constraints, failure design, verification, and deliberate learning with AI-assisted development.AI agent evaluationTask portfolios, trace grading, simulation, red teaming, stochastic reliability, and production quality gates.SEO and GEO for technical sitesAccurate discovery, retrieval, understanding, evidence absorption, citation, and measurement across search and answer engines.AI agent observabilityTracing model calls, retrieval, tools, memory, delegation, state transitions, evaluations, and human review.AI agent securityPrompt injection containment, tool permissions, identity, sandboxing, memory poisoning, and audit trails.Forward deployed engineeringTurning urgent field problems into production outcomes, reusable learning, and scalable product capability.Ontology and operational semanticsIdentity, relationships, state, provenance, and lifecycle semantics across CMDB, telemetry, and operational systems.
Growingtechnical book library
Question-ledpractical engineering guides
Evidence-firsteditorial method