Agent Strategy & Use Case Identification
Identify, score and prioritize agent opportunities using impact, feasibility, risk and roadmap criteria.
Explore topic →Explore the complete DataKnobs guide to Agentic AI: from strategy and architecture through context, memory, tools, security, evaluation, deployment, operations, reliability, learning and economics.
Choose the right opportunities and establish the lifecycle used to move them from idea to production.
Identify, score and prioritize agent opportunities using impact, feasibility, risk and roadmap criteria.
Explore topic →A visual closed-loop framework for planning, building, testing, deploying, operating and improving AI agents.
Explore topic →Design the core agent, reasoning behavior, prompts and runtime architecture.
Design the reasoning, orchestration, memory, tool, observability and governance layers of production agents.
Explore topic →Create durable system prompts, tool-use rules, error recovery instructions and provider-aware agent behavior.
Explore topic →Compare ReAct, Reflexion, Tree of Thoughts, Plan-and-Execute, ReWOO and other reasoning patterns.
Explore topic →Connect the agent to context, memory, tools, systems and other specialist agents.
Engineer context budgets, retrieval, RAG, GraphRAG, compression, isolation and knowledge layers.
Explore topic →Structure working, episodic, semantic and procedural memory with suitable storage and lifecycle controls.
Explore topic →Connect agents to APIs and systems using function calling, MCP, secure schemas and scalable integration patterns.
Explore topic →Choose orchestration patterns, coordinate specialist agents and manage multi-agent cost and complexity.
Explore topic →Control autonomy, protect systems, meet policy obligations and maintain accountable oversight.
Design approval gates, escalation tiers, async review workflows and selective autonomy for high-risk actions.
Explore topic →Define accountability, policies, evidence, controls and governance mechanisms across the agent lifecycle.
Explore topic →Protect against prompt injection, MCP risk, tool poisoning, memory attacks and privilege escalation.
Explore topic →Map NIST AI RMF to agents, quantify risk, prepare for regulation and govern high-impact execution.
Explore topic →Prove that agents work correctly and continue working when tools, models or environments fail.
Evaluate task completion, trajectories, components, LLM-as-judge results, regressions, cost and latency.
Explore topic →Build retries, circuit breakers, fallbacks, dead-letter queues, idempotency and chaos experiments.
Explore topic →Release, observe and operate agents continuously using production-grade controls.
Move agents safely into production with packaging, release controls, staged rollout and rollback planning.
Explore topic →Trace model calls, tools, memory and multi-agent handoffs using OpenTelemetry-compatible observability.
Explore topic →Run day-two operations with SLOs, incident response, on-call tiers, runbooks, capacity and cost controls.
Explore topic →Improve agent capability while balancing cost, ROI, learning and operational efficiency.
Improve agents through memory, fine-tuning, reinforcement learning, consolidation and skill evolution.
Explore topic →Manage token economics, model routing, cost per verified outcome, ROI, TCO and AI FinOps.
Explore topic →Use these guides as a connected blueprint for selecting use cases, designing the system, exposing the right controls, evaluating behavior and operating agents safely in production.
Explore Kreate, Kontrols and Knobs →