Building Your First Production Agent

1. Scaffold Your First LangGraph Agent Today

Use the 6-step pattern: StateDict → node functions → conditional edges fi PostgresCheckpointer fi MCP tool wiring → compile. Start with a simple 3-node graph (plan → execute → evaluate) before adding complexity. Run it against 20 test cases before touching memory or observability.

1. The Full Agentic Stack -- What We're Building Today

A production agent is not a single LLM call wrapped in a loop -- it is a layered system where each component has a clear responsibility. The five layers below are the mental model you need before writing a single line of

• LLM Brain n Tool The reasoning engine. Use model routing: nano for MCP-connected classification, mid-tier for generation, frontier only for sandbox, complex reasoning. 50-70% cost reduction vs support. always-using GPT-5.
• Memory Stack n Orchestrator → fi 5 layers: Working (context) Session (Redis) LangGraph Episodic (pgvector) fi Semantic (graph DB) fi node Procedural (system prompts). Write-Aside pattern super-step keeps p99 < 50ms. debug.
• Step 1 -- State Schema: Define a TypedDict with all fields the memory_refs, budget_remaining, iteration_count).

2. Instrument Before You Deploy

Add OTEL spans to all 5 layers before your agent sees production traffic. Set up Langfuse (free, self-hostable, MIT license) as your observability backend. Configure the 6 alert types: loop detection, budget breach, tool cascade, context drift, memory poisoning, delegation escalation. Agents you can't observe are agents you can't trust.

2. LangGraph 2.0 Scaffold -- The 6-Step Pattern

LangGraph 2.0 (released Feb 2026) codifies three years of production patterns. The framework's key insight: agents need a graph, not a chain -- they must retry, loop back for clarification, pause for human approval, and recover from failures. The 6-step scaffold pattern below applies to any agent type.

memory_refs, budget_remaining, iteration_count).

evaluate_output). Each takes State, returns State delta.

escalate_human), budget gates (if cost > cap → stop), and quality gates.

what enables resumption after failure and time-travel debugging.

(max 3 retries, exponential backoff). Never expose raw tool calls.

golden dataset (200+ pairs), assert < 2% regression before shipping.

• Step 1 -- State Schema: Define a TypedDict with all fields the memory_refs, budget_remaining, iteration_count).

3. Study Anthropic's Three-Agent Harness

Read Anthropic's engineering blog post on harness-design-long-running-apps. The Planner fi Generator fi Evaluator pattern with context resets is the production template for any task that spans more than a single context window. Implement structured handoff artifacts (JSON spec + progress file) for your own multi-step agents -- this alone will cut your silent failure rate by > 50%.

57% of organisations now run agents in production (up from 51% last year). Quality remains the #1 barrier (32%). LangGraph + LangSmith surpass 1B cumulative downloads -- the agent framework war is largely over at the orchestration layer. The next battle: which memory framework (Mem0 vs MemOS vs Oracle 26ai) becomes the default persistence layer. Gartner: 40% of enterprise apps will embed task-specific agents by EOY 2026, up from < 5% at start of year. The LLM Observability market hits $1.97B in 2026, on track for $6.8B by 2029 (36.5% CAGR) -- Agent SRE is the fastest-growing role in platform engineering.

ISSUE #24 automatic context reset triggers, and the emerging pattern of 'meta-agents' that monitor and restart failed sub-agents. Plus: first look at Anthropic Claude Mythos API

Varun's AI Learning Brief • Issue #23 • April 13, 2026 • varun.singla@outlook.com • Day 23 of Agentic AI Mastery Series

• Market Signal
• TOMORROW -- Advanced Agent Patterns: Self-Healing Agents -- circuit breakers, state recovery,

4. Memory Stack Wiring -- Write-Aside Pattern in Practice

Memory is what separates a stateless LLM call from a true agent. The Write-Aside pattern (Redis L1 sync fi pgvector L2 async flush fi Mem0 L3 unified API) keeps your agent fast while maintaining full persistence. Here is the production implementation in four concrete steps:

decision writes to Redis with a Memory ID. p99 write latency < 5ms.

10). Embeddings use text-embedding-3-small. Retrieval: cosine top-5.

graph-enhanced retrieval (48K+ stars) gives 3-5× better personalisation than naive vector search.

erasure request. Namespace isolation (Postgres schemas + Redis ACLs) prevents multi-tenant leakage.

Stored in append-only WORM storage for EU AI Act Annex III compliance.

• L1 -- Working Memory (Redis, TTL 24h): Every tool call result, decision writes to Redis with a Memory ID. p99 write latency
• L2 -- Episodic Memory (pgvector async): An async worker flushes 10). Embeddings use text-embedding-3-small. Retrieval: cosine
• L3 -- Unified API (Mem0): Mem0 sits above both stores and graph-enhanced retrieval (48K+ stars) gives 3-5× better personalisation
• GDPR Compliance: Every memory write logs a Memory ID. erasure request. Namespace isolation (Postgres schemas +
• NIST CAISI Audit Trail: Every read AND write is logged with Stored in append-only WORM storage for EU AI Act Annex III

5. OTEL Instrumentation -- What to Instrument and Why

OpenTelemetry (OTEL) is the lingua franca of agent observability. Every span you emit flows to your chosen backend (Langfuse, LangSmith, Arize) without vendor lock-in. The key is the OpenInference semantic conventions -- standardised attribute names for LLM spans that make dashboards interoperable. Instrument

enforce model routing -- mid-tier should handle > 70% of calls.

• Agent Health span: active_agent_count, trace_volume, budget_burn_rate, kill_switch_status. Alert if burn
• Tool Call span: tool_name, call_frequency, failure_rate, latency_p99, parallel_ratio. Alert on > 3 consecutive
• LLM span: model_tier, token_usage (prompt+completion), model_errors, cache_hit_rate. Use this to
• Memory span: vector_db_latency, cache_hit_rate, nist_audit_compliance_pct, memory_write_count. Alert if
• Drift span: input_distribution_drift (alert > 15%), llm_judge_score_trend, tool_call_sequence_anomaly,