advanced
14 min read
A2A Explained: How Agent-to-Agent Communication Differs from MCP
MCP answers "what can I call?" A2A answers "who can I hand this to?" Different questions, different failure modes.
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Data Contracts
Explicit schemas, types, validation rules, and boundary semantics that make data exchanged between components predictable.
Tagged articles
Data Contracts articles
25 articles in this tag.
advanced
14 min read
Advanced Python Typing: TypedDict, Literal, Protocol, Generics, and Callable Types
A dict payload that passes every test until someone renames a key. A callback whose signature drifts by one argument. A duck-typed object that satisfies…
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advanced
17 min read
Agent Execution Environments: Filesystems, Shells, Browsers, Code, Databases, and Sandboxes
The demo worked because the agent was running on your laptop. It had your credentials, your network, your home directory, and your shell history. Move it…
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advanced
16 min read
Agent State and Persistence: Typed State, Checkpoints, Stores, Threads, and Artifacts
Kill a long agent mid-task and you learn which of those two things your system actually believes. If the process dies and the transcript survives but the…
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advanced
15 min read
The Anatomy of an Agent Loop: State, Action, Observation, Feedback, and Termination
The demo works. The agent books the flight, summarizes the repo, files the ticket. Then you point it at a task with one extra branch, and it calls the same…
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advanced
15 min read
Designing a Deep Research Agent: Search, Evidence, Verification, and Bounded Revision
You have seen the failure. A research agent returns a polished report, the citations look real, and the first URL you check resolves to a page that does…
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advanced
13 min read
Designing a Workspace Operations Agent: Tools, State, Persistence, and Human Approval
The demo works. The agent reads the queue, drafts the update, and posts it. Then someone asks who approved the change, which run produced it, and how to…
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advanced
14 min read
Designing an Adaptive Knowledge Agent: Routing, Retrieval, Memory, and Context Assembly
The demo works on turn one. By turn five, the agent is answering a question you already answered, citing a document it retrieved three turns ago, and…
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advanced
15 min read
Evaluator Design and Feedback Contracts: Graders, Judges, Disagreement, and Targeted Retry
An evaluator is only as useful as the set of distinct repair actions its output can select.
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advanced
13 min read
Human Feedback Loops: Approval, Grading, Editing, Escalation, and Resumption
The demo works. The agent reaches a consequential step, pauses, and asks a human to approve. The human clicks approve. Then the resumed run re-executes the…
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advanced
10 min read
Jev and Laya Explained: How Non-Autoregressive “System One” Decision Models Work
You have a routing step in an agent pipeline. Four possible destinations. The LLM call costs a full generation, 1–2 seconds, and a token bill. The answer…
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intermediate
13 min read
Modern Python Type Hints: What Annotations Do—and Do Not Do
Here is a function that lies to every tool you own and still runs in production:
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advanced
15 min read
Planning and Scheduling Harnesses: Task Graphs, Dependencies, ReWOO, and LLMCompiler
A plan that reads like a clean numbered list can still deadlock at runtime, because the list never told the scheduler which steps actually depend on which…
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advanced
13 min read
Prompt Chaining: Designing Multi-Stage Cognitive Workflows
The demo works. You paste a gnarly task into one prompt—extract the entities, reconcile them against the schema, flag the contradictions, write the…
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advanced
15 min read
Prompt Contracts: Objectives, Inputs, Constraints, Outputs, and Acceptance Criteria
A prompt that passed review, shipped, and then quietly broke a downstream consumer is not a prompt problem. It is a specification problem wearing a prompt…
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intermediate
13 min read
Prompt Engineering as Interface Design: From Vague Requests to Explicit Behavior
The prompt worked on your laptop. In production, the same prompt returns a paragraph on Monday, a bulleted list on Tuesday, and a confident fabrication on…
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intermediate
14 min read
Python Dataclasses and Structured State: Designing Explicit Data Contracts
I have watched the same bug arrive in three different codebases. A configuration payload enters as a dictionary, gets copied into a queue message, gets…
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advanced
15 min read
Python Decorators Under the Hood: Wrappers, Closures, and Function Transformation
A decorator is not a wrapper. It is a rebinding that happens once, at import time — and most decorator bugs live in the gap between that fact and the…
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advanced
13 min read
Python Multiprocessing and Process Pools: When Separate Processes Are Worth It
One core is pinned at 100%. Fifteen sit idle. You wrap the loop in a pool, run it again, and the wall clock barely moves — or gets worse. Nothing about the…
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advanced
16 min read
ReAct Agent Loops: Tool Use Through Thought, Action, and Observation
An agent that acts without reasoning is not reasoning. It is guessing with a tool schema.
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advanced
15 min read
Reflection and Self-Refine: Building Generator-Critic Revision Loops
You add a critique-and-revise step. The output gets longer, more confident, more padded with caveats. Your eval score does not move.
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advanced
16 min read
Structured Outputs as Data Contracts: JSON Schema, Validation, Repair, and Retry
The 2 a.m. page is almost always the same story. A pipeline that extracted clean JSON in the demo starts throwing parse errors in production, and the logs…
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advanced
16 min read
Tool Engineering for AI Agents: Schemas, Descriptions, Contracts, Errors, and Idempotency
A failed agent run rarely looks like an interface problem. It looks like a model problem: wrong tool, malformed arguments, an error swallowed and retried…
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advanced
14 min read
What Is Graph Engineering for AI Agents? From Prompt, Context, Harness, and Loop Engineering to System Intelligence
The demo works. Then you add a second worker, a shared artifact store, and a verifier, and the system starts producing results nobody can attribute,…
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advanced
12 min read
Workflow vs Agent: Deterministic Control and Model-Directed Decisions
The demo worked. Production didn't. Somewhere between the two, the system took a path nobody wrote down, and when it failed there was no artifact…
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