Production AI Agents
The hard engineering problems behind shipping reliable AI agents: memory, concurrency, retries, observability, and failure handling in production.
13 conceptsInteractive diagrams
01
What Makes Agentic Systems Uniquely Hard
Why agents break the assumptions of classic request-response software engineering.
02
The Anatomy of an Agent Loop
The think-act-observe cycle and where each component can fail in production.
03
Why Classic Software Engineering Still Wins
The deterministic principles that must underpin any reliable agentic system.
04
Memory Management
Short-term, long-term, and external memory strategies for stateful agent behaviour.
05
Concurrency
Running multiple agent tasks in parallel safely without races or budget overruns.
06
Backpressure
Preventing agent loops from flooding downstream systems with uncontrolled throughput.
07
Retries
When to retry LLM calls, tool calls, and actions and how to avoid infinite loops.
08
Timeouts
Setting deadlines at every layer so a hung tool call cannot stall the entire agent.
09
Failure Handling
Classifying errors, deciding when to abort vs recover, and surfacing failures clearly.
10
Observability
Tracing, logging, and metrics for understanding what an agent actually did in production.
11
The Reliable Agent Stack
A reference architecture combining the patterns above into a production-ready agent system.
12
Failure Mode Catalog
A catalogue of the most common ways agents fail and the mitigation for each.
13
Engineering Principles That Never Go Away
Idempotency, least privilege, fail-fast, and the other fundamentals that survive every paradigm shift.