01 Pressure
Goal arrives
Intent, files, context, budget, and risk enter the same loop.
scopeAI-native systems engineering
demo clean / production messy
Notes on building systems that hold up in production.
Notes on retries, state, budgets, coordination, observability, and failure recovery — the details that decide whether pressure becomes progress or repeat pain.
distributed systems production agents data platforms observability
production-agent-loop runtime map
Core question Can the loop recover when tools, cost, and memory drift?
02 Control
Policy bounds
The runtime chooses tools, retries, memory, and stop conditions.
bounds03 Signal
Telemetry answers
Traces expose latency, token spend, tool errors, and bad branches.
p9504 Library
Learning captured
Failures become evals, docs, guardrails, and better defaults.
replay How an agent survives production pressure A production agent is not a chat box. It is a bounded control loop with state, tools, policy, observability, and replay.
10B+ events/day systems direction
150+ paying users from zero
70+ data pipelines shipped
1B+ market data points served
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agents
AI systems that survive real users
Multi-agent orchestration, bounded tool calls, memory, evals, cost guardrails, and recovery paths.
systems
Distributed systems with operational taste
Coordination, retries, idempotency, stream processing, failure detection, SLOs, and quiet incident prevention.
platforms
Data platforms that earn their keep
Kafka, Spark, ClickHouse, search, lakehouse migrations, and the product pressure behind architecture choices.
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01 · Flue Under the Hood: Why This Agent Harness Holds A concrete engineering pattern, stripped down to the decision that matters. 02 · The Cost of Context: Why a Bigger Token Window Is a Trap A concrete engineering pattern, stripped down to the decision that matters. 03 · The Model Updated. Now What? A concrete engineering pattern, stripped down to the decision that matters. 04 · The Invisible Technical Debt of AI Systems A concrete engineering pattern, stripped down to the decision that matters.
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Deep dives
Harness Engineering: The Compounding Stack The operating layer around serious AI work. Harness Internals A concrete engineering pattern, stripped down to the decision that matters. Production Agents Deep Dive Retries, state, cost, security, and evals after the demo. AI Engineering Fundamentals Start at tokens; end at evaluated agents.