All 71 articles, sorted alphabetically
Agent Evaluation at Scale
From spot checks to continuous monitoring: trace capture and replay, LLM judges with calibration, drift detection, reliable eval datasets, CI/CD metri…
Read article →Agent-to-Human Handoff Patterns, in depth: triggers, ownership, the context packet, routing and handing back
How to transfer a live conversation from an AI agent to a human without dead air, double replies or re-asking: handoff triggers, an ownership state ma…
Read article →Agent Memory Layers Compared
Working, episodic, semantic — what to persist where. Three tiers of agent memory, from the live context window to long-term facts, and why each tier m…
Read article →Agent Memory Systems, in depth: who controls memory, the service contract, scoping, concurrency and running it in production
The engineering of an agent memory system: agent-directed memory tools versus harness-directed hooks, a memory service API with versions and idempoten…
Read article →Multi-Agent Debate Architecture, in depth: rounds, roles, aggregation, stopping rules and when debate is worth its cost
How multi-agent debate works and how to build it: independent first answers, critique rounds, devil's advocate and judge roles, majority versus j…
Read article →Agent Skill Anti-Patterns: Scope Creep, False Positives, and Prompt Bloat
What actually goes wrong with agent skills in practice: descriptions broad enough to cause false-positive matches, a single skill trying to do too muc…
Read article →Agent Skills Architecture: Decoupling What an Agent Does from How It Runs
Deep-dive on the agent skill pattern: a portable, inert instruction file that decouples an agent's behavior from its runtime, why that decoupling…
Read article →Skill Discovery and Retrieval: How an Agent Runtime Picks the Right Skill
Deep-dive on how an agent runtime matches an incoming request against a catalog of skill description fields: embedding similarity vs. LLM-as-router vs…
Read article →Versioning and Composing Agent Skills Without Breaking the Other 49
Deep-dive on treating agent skills as independently-versioned artifacts: what counts as a breaking change to a skill's trigger conditions or outp…
Read article →Skills vs. Tools vs. Fine-Tuning: Where Each Actually Belongs
A decision framework for choosing between a skill (in-context instructions), a tool (a callable function with a schema), and fine-tuning (a weight-lev…
Read article →Capability Fallback Chains: What an Agent Does When Its Best Tool Isn't Available
Designing explicit fallback chains for agents when a primary tool, model, or skill is unavailable -- specialist tool to generic tool to human -- why s…
Read article →Observability and Tracing for Agentic Systems
Distributed tracing across a multi-step, multi-model agent pipeline: a span per LLM call and per tool call, parent-child relationships across a plan-e…
Read article →Permission Boundaries for Autonomous Agents: Capability Scoping, Not Just Auth
The difference between authenticating an agent and scoping what a specific agent run is allowed to do right now: least-privilege per-task credentials …
Read article →The Skill Registry Pattern for Multi-Agent Fleets
Deep-dive on sharing a common skill catalog across a fleet of agents: central registry versus per-agent local copies, how an agent advertises which sk…
Read article →Testing Agent Skills Before They Reach Production: A Validation Framework
A validation framework for agent skill files specifically: golden-example test sets, regression testing on skill edits, catching the false-positive pr…
Read article →Choosing Your Agentic Stack: A Decision Framework
A practical framework for picking an orchestration framework, memory/vector store, observability tooling, and context/RAG library together as a cohere…
Read article →Context and RAG Tooling for Agents
Comparing LlamaIndex, LangChain, and Haystack as libraries for building an agent's retrieval and context-assembly layer -- what each is actually …
Read article →Memory and Vector Store Options for Agentic Systems
Comparing pgvector, managed vector databases, and self-hosted open-source vector databases for agent memory -- operational trade-offs, and the specifi…
Read article →Observability Tooling for Agents
Comparing purpose-built LLM/agent observability platforms against a custom OpenTelemetry-based tracer -- what each captures, and when off-the-shelf to…
Read article →The Agentic Orchestration Stack Compared
Comparing LangGraph, CrewAI, AutoGen, and Google's ADK by core design philosophy -- explicit state graph vs. role-based crew vs. conversational m…
Read article →Agent checkpointing, in depth: step boundaries, write ordering, idempotent resume, human interrupts and forking
Agent checkpointing in depth: what a checkpoint holds, committing at step boundaries, a storage schema, idempotent side effects, human interrupts, tim…
Read article →Context compaction for long-running agents
Deep-dive on context compaction: keeping a long agent session inside the context window by summarizing settled history rather than truncating it. Cove…
Read article →Context engineering architecture
Deep-dive on context engineering for agents: per-turn token budgets, history windowing and commitment-preserving compaction, tool-result hygiene and a…
Read article →Agent cost optimization -- controlling the token bill
Deep-dive on agent cost optimization: why agents are token-hungry (loops, growing context), the cost drivers, model routing, context management, promp…
Read article →Agent Evaluator Architecture, in depth: the grading contract, state checks before judges, pass^k, and evaluating the evaluator
How to build the component that decides whether an agent run succeeded: the evaluator's input and output contract, grading final environment stat…
Read article →Agent Frameworks, in depth: the six parts every framework implements, a 40-line runtime that shows them, and how to decide what to adopt
What an agent framework actually does: the control loop, model adapter, tool layer, state and checkpoints, orchestration and observability, shown in a…
Read article →Agent guardrails
Deep-dive on agent guardrails: input/output/action/budget control points, deterministic vs LLM-based checks, fail modes (block/warn/escalate), defense…
Read article →Human-in-the-loop -- humans and agents collaborating
Deep-dive on human-in-the-loop agents: approval gates, escalation, review/correction, and confidence-based involvement, interaction patterns (sync/asy…
Read article →Agent memory architecture, in depth: the memory lifecycle of scoring, reconciliation, consolidation, forgetting and safety
Agent memory as a lifecycle rather than a store: the memory record schema, retrieval scoring with relevance, recency and importance, per-kind decay, r…
Read article →Memory Pattern, in depth: short-term thread state and long-term memory as two persistence problems in one agent loop
The agent memory pattern built from first principles: why thread state and long-term memory need different contracts, where each hooks into the agent …
Read article →Multi-Agent Pattern, in depth: when to split one agent into several, topologies, typed contracts and the token arithmetic
An engineer's guide to the multi-agent pattern: what it is, why context isolation is the real benefit, what you lose in shared context, the evide…
Read article →Multi-Agent Orchestration Architecture in Depth
Multi-agent orchestration architecture in depth: choosing a topology, a capability registry with tiered routing, typed handoffs by reference, an owned…
Read article →Agent orchestrator architecture, in depth: task graphs, the scheduler loop, budgets, validation, cancellation and interrupts
How to build the coordinator at the centre of an agent system: a task graph with explicit node states, bounded dynamic planning, a scheduler loop with…
Read article →Agent output verification architecture
Deep-dive on verifying agent outputs before acting: structure, grounding, and policy layers; deterministic rules vs LLM-as-judge; the pass/repair/reje…
Read article →Agent planner architecture
Deep-dive on agent planner architecture: the reflection loop with state store, memory, guardrails, and observability.
Read article →Agent planner v2 architecture, in depth: plan-or-act gating, two-tier plans, budget ledgers, local repair and eval-gated rollout
How to upgrade an LLM agent planner from a single-shot flat plan to a v2 design: a gate that decides whether to plan at all, strategic milestones with…
Read article →Agent Planning Architecture in Depth: choosing between ReAct, plan-and-execute, DAG planners and tree search
Agent planning as search: four strategy families with a cost model, a shared runtime with budgets and verifiers, a worked comparison on one task, a st…
Read article →Agents in Production, in depth: outcome SLOs, versioned behaviour bundles, runtime budgets, release gates, an incident catalogue and the on-call runbook
How to operate LLM agents in production: SLOs defined on outcomes, one versioned and hashed bundle for prompts, models, tools and policies, step, toke…
Read article →ReAct -- reasoning and acting interleaved
Deep-dive on the ReAct agent pattern: interleaving reasoning and acting (thought/action/observation), the loop, grounding in real results, adaptivity …
Read article →Agent Reflection Architecture, in depth: grounded critique, the revise loop controller, stop rules and when reflection makes answers worse
How to build within-episode reflection into an agent: what reflection is and is not, where a trustworthy critique signal comes from, a structured crit…
Read article →Agent request router architecture
Deep-dive on request routing for agent systems: a cheap classifier that scores intent and difficulty, a routing policy that dispatches to a cheap mode…
Read article →Agent tool sandboxing -- containing what agents can do
Deep-dive on agent tool sandboxing: the untrusted-agent threat (LLM-driven, prompt-injectable), sandboxing tool execution, isolation levels (process/c…
Read article →Agent semantic cache architecture
Deep-dive on semantic caching for LLM agents: embedding-based nearest-neighbor lookup, the similarity threshold as the precision/hit-rate dial, scope …
Read article →Speculative tool execution for agents
Deep-dive on speculative tool execution in LLM agents: predicting the next tool call, running read-only tools eagerly in a sandbox, committing on a ma…
Read article →Agent state machine architecture
Deep-dive on modeling an LLM agent as an explicit finite state machine: named states (intake, plan, act, observe, verify, respond, escalate, error) wi…
Read article →Agent task decomposition
Deep-dive on agent task decomposition: a decomposer that plans subtasks and dependencies, a scheduler that releases the ready set and exploits paralle…
Read article →Agent tool-call recovery architecture
Deep-dive on making agent tool calls robust: an error taxonomy (transient / invalid-input / permanent), idempotency keys for safe replay, exponential …
Read article →Agent tool schema versioning architecture
Deep-dive on versioning agent tool schemas: why a schema is a prompt not an API contract, immutable versioned registries, compatibility classification…
Read article →Tool selection architecture
Deep-dive on dynamic tool selection (tool retrieval) for agents whose catalog has grown to hundreds or thousands of tools, where listing them all in t…
Read article →Agent tool use architecture, in depth: registry, policy gate, parallel dispatch and result shaping
How an agent runtime turns model tool calls into safe, bounded actions: the registry, choosing which tools to expose, validation and a policy gate, pa…
Read article →Tools Pattern, in depth: the six shapes a tool can take, a framework-neutral tool layer, middleware composition, jobs, agents and humans behind the same interface, and how to expose it all over MCP
A framework-neutral guide to the tools pattern for agents: the contract between model and runtime, six tool shapes (query, command, job, handle, agent…
Read article →Typed Decisions in Agent Pipelines: Routing, Moderation and Extraction with a Schema-Constrained Decision Model such as Jev
Implementing an agent's tool selection, moderation of inputs, tool results and outputs, and argument extraction as typed Choice, Score and Noul q…
Read article →Durable Agent Workflows with State, Retries, and Replay
How durable execution makes long-running agents survive crashes, deploys and multi-day human waits: deterministic orchestration versus activities, wha…
Read article →Human Approval Gates in Autonomous Agent Systems
How to build approval gates an agent cannot talk its way past: enforcement in the tool executor, approvals bound to a digest of the exact action, revi…
Read article →LangGraph for Agent Orchestration, in depth: state and reducers, routing with Command and Send, checkpoints, interrupts and failure modes
How LangGraph actually executes an agent: typed state with reducers, super-steps, conditional edges, Command and Send for routing and fan-out, thread-…
Read article →Memory Architectures for Long-Running AI Agents
How to give an agent that works for hours or days a task memory it can trust: a ledger, a decision log, a fact store with provenance and supersession,…
Read article →Multi-Agent Collaboration & Negotiation: Orchestrating Collective Intelligence
Multi-agent collaboration and negotiation in depth: the Contract Net Protocol, second-price auctions, alternating-offer bargaining, joint commitments,…
Read article →Multi-Agent Communication Protocols: The Language of Collaboration, in depth: envelopes, speech acts, interaction protocols and delivery guarantees
How agents should talk: the transport, envelope and interaction layers, FIPA-style performatives, request and contract-net state machines, idempotent …
Read article →Multi-Agent Systems: What Happens When a 'Developer' LLM Talks to a 'Reviewer' LLM?
Individual AI agents, equipped with planning capabilities and tool-use (as discussed in previous articles), are remarkably powerful.
Read article →ReAct Pattern Explained, in depth: building the Thought-Action-Observation loop from scratch, parsing, native tool calling, guards and failure modes
A hands-on guide to the ReAct agent pattern: what the original paper tested and found, a worked trace, a from-scratch text-protocol implementation wit…
Read article →ReAct Pattern Revisited, in depth: native and parallel tool calls, reasoning models, the token economics of the loop and when a workflow should replace it
ReAct four years on: what survived from the text Thought-Action-Observation format, how native and parallel tool calls and reasoning models change the…
Read article →Sandboxing Code-Executing Agents with Policy Boundaries
How to turn a sandbox for agent-written code into an enforceable policy: derive a per-task policy outside the model, compile it into container flags, …
Read article →Deploying Agentic Systems
System design for running autonomous agents in production: sandboxing execution, why minutes-long agent runs break normal request-response autoscaling…
Read article →Cost and Latency Budgets for LLM Products
A system design framework for budgeting cost and latency across an LLM product before building it: decomposing end-to-end latency and cost-per-request…
Read article →LLM Gateway Architecture
System design for the infrastructure layer between applications and LLM providers: request routing by cost/latency/capability, rate-limit pooling acro…
Read article →Designing a RAG Pipeline at Scale
System design for a production RAG pipeline: ingestion and chunking trade-offs, embedding generation and batching, the index write path, retrieval and…
Read article →Vector Search Infrastructure
System design for vector search at scale: index-type trade-offs (HNSW vs IVF vs flat), sharding an index across nodes, recall versus latency versus me…
Read article →The Rise of 'Thinking' Models: How Chain-of-Thought (CoT) Is Turning LLMs into Logic Engines
Large Language Models (LLMs) have captivated the world with their ability to generate fluent, coherent, and often creative text.
Read article →Tool Calling Best Practices, in depth: designing the tool surface a model sees, from granularity and names to schemas, error messages, return payloads and evals
How to design tools that language models call correctly: choosing tool granularity, naming and namespacing, writing descriptions, constraining input s…
Read article →Tool-Calling Reliability: Timeouts, Idempotency, and Compensation
How to make an agent's tool calls safe when they time out: treat the unknown outcome as a first-class result, propagate deadlines from the turn b…
Read article →Tool Use Failure Modes, in depth: a taxonomy you can detect in traces, measure in evals and fix at the right layer
A practical taxonomy of how LLM agents fail at tool use: skipped calls, wrong tools, invented and ungrounded arguments, bad sequencing, misread result…
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