Building Agentic AI Systems That Survive Production
Agent frameworks demo well and collapse in production. A field guide to the tool schemas, memory, evaluation and observability patterns that actually hold up.
The demo-to-production gap
A recorded LangGraph or CrewAI demo shows five agents debating a task and shipping a beautiful output. Production is a single agent, three tools, one supervisor loop, and a strict token budget. The gap between the two isn't intelligence — it's engineering discipline. Every agentic system we ship starts by cutting the number of agents in half and doubling the specificity of each tool schema.
Tool schemas are the real product
The single biggest determinant of agent reliability is the quality of tool schemas. Vague descriptions like 'search_database' produce vague behaviour. We write tool descriptions the way we write API docs: preconditions, exact input shape, expected output, failure modes and one worked example. A well-described tool with a smaller model outperforms a poorly-described tool with a larger one — every time.
Memory is a design decision, not a feature
Most 'agent memory' implementations are unbounded conversation logs stuffed back into context. This burns tokens and degrades reasoning. Production memory is stratified: short-term working memory in the current context, medium-term summaries per session, long-term facts written to a vector store with explicit write policies. Every write should be a deliberate act, not a side effect of the loop.
Supervisor loops beat multi-agent debates
The multi-agent debate pattern is fashionable and expensive. We consistently ship better outcomes with a single supervisor agent that plans, delegates to specialist tools, and self-critiques against a rubric — with a hard step budget. When you're tempted to add a second agent, add a tool instead.
Evaluation is a product surface
Every agent we ship has an evaluation harness that runs on every prompt or tool change: task-completion rate, tool-call precision, hallucination rate, cost per successful task. These metrics are wired into the deploy pipeline as gates. No eval, no deploy — regardless of who is asking.
Observability separates hobbyists from operators
Traces per run, per tool call, per token, with structured error taxonomies — this is the difference between an agent you can debug at 2am and one you have to babysit at 2pm. We instrument with OpenTelemetry-style spans and ship a Grafana board on day one, not month six.
Cost control is not optional
An unmonitored agent will happily spend $40 solving a $2 problem. Every deployment ships with per-agent, per-tenant budget alerts, a routing layer that picks the smallest model that will succeed, and a hard step limit. If the CFO can't answer 'how much did we spend on agents last week' in one query, you don't have production.
Key takeaways
- Halve the number of agents; double the specificity of every tool
- Treat memory as an explicit design decision with write policies
- Supervisor + specialist tools beats multi-agent debate
- Ship an eval harness as a deploy gate on day one
- Instrument for cost the same way you instrument for latency
T7 Research
Enterprise AI Research Group
T7 Research is the research arm of T7 Solution, focused on benchmarking LLMs, evaluating RAG patterns, and compiling implementation playbooks for enterprise technology leaders.