“agents”
Building an A2A Client in Python: A Hands-On Guide
A code-along guide to building an A2A client in Python with the official SDK: resolve the Agent Card, send messages, stream status and artifact updates over SSE, handle input-required multi-turn tasks, and secure push notifications.
A2A Agent Cards: Publishing and Resolving Agent Capabilities
Author, publish, sign, and consume A2A Agent Cards: the JSON manifest that declares an agent identity, skills, interfaces, and security requirements at /.well-known/agent-card.json, with a worked publishing and resolution example.
Root Cause Analysis Playbooks for Agent Failures: From Symptom to Fix
A systematic method for debugging agent failures: classify the symptom, walk a trace from the failing span upward, and apply a playbook for each failure class — routing, tool, context, hallucination, and infrastructure — with durable execution and state inspection as first-class techniques.
Delegated Authorities: Scoped Permissions for Sub-Agent Execution
Give sub-agents only the authority their task requires: why tool lists are not permissions, how agents-as-tools and handoffs differ in privilege, building a delegation layer with capability objects, and enforcing scope at the runner rather than trusting the prompt.
Correlation IDs Across Agent Handoffs
Keep a single identifier alive as control moves between agents: why log IDs break at handoffs, how OpenTelemetry context propagation and baggage carry correlation across boundaries, the security rules for propagating IDs, and a concrete schema for multi-agent trace correlation.
Trace Spans for Tool Calls: Instrumenting Function Execution
Instrument agent tool calls with OpenTelemetry spans: span structure, trace and span IDs, parent relationships, attributes versus events, status codes, wrapping sync and async tools, and the schema that makes tool spans queryable instead of just present.
Consent Flows: Human Approval at the Point of Action
Design consent flows that pause an agent before it acts: deciding which tools need approval, the pause-and-resume state machine, sticky decisions, rejection messages, server-side approval security, and how to build the same pattern on the Gemini function calling loop.
Error Handling for Tool Calls: Retryable vs. Fatal Errors
Classifying agent tool failures into retryable and fatal buckets — how the OpenAI Agents SDK surfaces errors to the model, configures retries, and when never to retry at all.
Financial Advisory Agents: Data, Disclaimers, and Regulated Outputs
Building AI agents that touch investment topics without hallucinating numbers or crossing regulatory lines — grounding, structured outputs, safety settings, and human review with the Gemini API.
Namespacing Agent Memory Stores Per Tenant: Isolation Without the Leak
Hard namespaces vs. metadata filters for multi-tenant agent memory in LlamaIndex — why filter-only isolation leaks, and how to combine both layers for defense in depth.
Chaos Testing Agent Workflows: Breaking Things on Purpose
How to apply chaos engineering to agent workflows — fault injection for model outages, tool failures, and stalls, plus the observability needed to prove resilience.
Streaming vs. Polling: Response Patterns for Async Agent Clients
When to stream agent responses, when polling is the right answer, and how to combine them with server-sent events, WebSockets, and durable run state.
A2A vs. MCP: Choosing the Right Interop Standard for Your Agents
MCP is vertical, A2A is horizontal. A practical decision framework for when your agent needs tool access, when it needs peer collaboration, and how the two compose.
Decision Logs as First-Class Data: Post-Mortems for Multi-Agent Systems
Treat agent decisions as structured, queryable data using OpenTelemetry logs and traces — and turn post-mortems into an automated, evidence-driven practice.
Alerting on Agent Anomalies: Drift, Hallucinations, and Stalls
A practical alerting strategy for production agents — what to detect, what thresholds to set, and how to wire LiteLLM alerting into Slack, email, and webhooks.
AG-UI with CopilotKit: Streaming Agent State into Your App UI
How the AG-UI protocol and CopilotKit connect custom frontends to agents, and why day-zero A2UI support makes multi-agent UI rendering practical.
Caching for Agentic RAG: Index Refresh and Query Caching
Agentic RAG loops re-retrieve constantly. Layer query caching, response caching, and index refresh policies to cut latency and LLM bills without serving stale answers.
Escalation Ladders: Handing Failed Tasks to Humans with Full Context
Autonomous agents fail. Design escalation ladders that detect failure, retry intelligently, and hand work to humans with the full context needed to finish in one pass.
Policy Enforcement Layers for Autonomous Agents: Guarding Every Tool Call
One guardrail is not a security model. Layer input validation, tool scopes, runtime policy checks, and MCP authorization so every agent tool call passes through defense in depth.
PydanticAI vs. OpenAI Agents SDK: A Side-by-Side Build Comparison
Build the same triage agent twice - once in PydanticAI, once in the OpenAI Agents SDK - and compare typing, tools, guardrails, sessions, and observability.
Visualizing Agent Trajectories: Debugging Loops and Dead Ends
Agent logs lie by omission. Learn to render trajectories as graphs, spot infinite loops and dead ends, and turn run visualization into a debugging practice.
Progress Visibility: Streaming Agent Steps and Thoughts to Users
Why silent multi-second agent runs destroy UX, and how to stream step lifecycles, tool calls, and reasoning to the client with the OpenAI Agents SDK, SSE, and MCP-style progress notifications.
Caching Tool Results to Shorten Agent Trajectories
Agent trajectories grow expensive fast because every tool call costs a model round trip. Learn what is safely cacheable, how to key the cache, where to place it in a LangGraph graph, and how to measure the wins.
CrewAI in Depth: Roles, Tasks, and Process Orchestration
A practical tour of CrewAI's core abstractions — agents, tasks, processes, and Flows — with guidance on when this role-shaped framework beats a graph-based one.
Idempotent Tool Design: Making Every Agent Call Safe to Repeat
Agents retry constantly, and naive tools double-charge or duplicate rows when they do. Apply Stripe-style idempotency keys, natural keys, and dedupe patterns so every tool call is safe to repeat.
Genkit Agents API for Dart: Full-Stack Conversational AI in Flutter
How the Genkit Agents API brings full-stack conversational AI to Dart and Flutter — defining agents on the server and driving them from Flutter with a shared chat() interface.