Notes from the trenches on building production-grade, multi-agent AI systems, where the frontier meets the fundamentals of good software engineering.
Skills describe, local tools execute in-process, and MCP crosses the process boundary. What each layer costs in latency, coupling, isolation, and context, how to swap backends behind a stable contract, and a decision tree for new tool requests.
Read the article →Four structural failure modes behind conflicting facts, deadlocks, and cascading errors, and the fixes: typed shared state with field ownership, validated task graphs, three-layer output gateways, fact reconciliation, circuit breakers, tracing, and canary releases.
Read the article →The tool-calling lifecycle and five safety checkpoints, MCP, context compression, retrieval internals, three-tier memory, multi-agent state, and ablation experiments: what a prototype answer sounds like, and what a production answer sounds like.
Read the article →Prompt, context, skills, loop, and graph engineering: what each rung solves and where it breaks, why loops and graphs coexist in the same harness, a case study of a mixed graph-loop assistant, and why agent graphs are more than a rebranded DAG.
Read the article →Why most agent portfolio projects get screened out in three seconds, and how to fix them: five engineering pitfalls from mismatched storage to copy-paste use cases, an upgrade matrix for refactoring a prototype into a production system, and an agent-centric STAR template for describing it.
Read the article →Vector RAG answers single-hop questions well and breaks on anything that spans documents. A production Graph RAG architecture: a three-step graph construction engine, provenance on every triplet, intent-driven routing, hybrid graph + vector retrieval with one reranker, white-box citations, and the DevOps that keeps a live graph healthy.
Read the article →Measuring agent quality is two jobs, not one: prove a change is good enough to ship, then catch a system you haven't touched decaying anyway. Automated vs. human eval as a division of labor, the pre-release gate (offline regression, shadow, canary, A/B), and monitoring degradation and distribution drift once real users — and a model you don't control — start moving under you.
Read the article →Evals are the test suite for a probabilistic system. Why outcome and trajectory both matter, why consistency (pass^k) is a different axis than raw capability (pass@k), how to grade with rules and a calibrated LLM-as-judge without the biases, and how evals graduate from capability checks into a regression gate that runs on every commit.
Read the article →These aren't three competing techniques; they're three stages of one pipeline: retrieve wide, fuse, rerank narrow. How semantic search, hybrid search (BM25 + dense, fused with RRF), and cross-encoder reranking fit together, and the two places they pay off hardest inside an agent: semantic caching and runtime tool selection.
Read the article →A guardrail is just input validation and output sanitization wrapped around an inference layer. How input and output guardrails work, the four ways to implement them (rules, classifiers, LLM-as-judge, guard models), and why the synchronous-versus-asynchronous decision quietly shapes your agent's latency, safety, and UX.
Read the article →Memory feels solved with one agent and a markdown file, then falls apart the moment you add a second agent or a hundred facts a day. How agents share state, resolve conflicting facts, and make knowledge discoverable without hand-wiring aren't AI problems; they're distributed systems problems, and the teams treating them that way ship agents that work.
Read the article →The more you build with agents, the more they rhyme with mature software engineering. A concept-by-concept mapping (A2A to service discovery, MCP to REST, RAG to the database, Eval to the test suite), and why making agents reliable is an architecture problem, not a model problem.
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