Service 02

Multi-Agent AI Systems

Production AI agent pipelines: stateful, resilient, monitored.

Deliverables
  • Agent graph tested, documented, deployed
  • Operational monitoring with defined SLA
  • Resilience test suite (timeouts, retries, fallbacks)
  • Graph documentation and incident runbook
  • Langfuse dashboards (traces, costs, alerts)
Working method
[phase] mapping
agent(s)backend-api
skill(s)grill-me
inputprocess_description · io_examples · risk_tolerance
→model_current_process()
→identify_human_decisions()
→scope_automation_perimeter()
outputprocess_map · node_list · edge_cases[]
[phase] graph_design
agent(s)backend-api
skill(s)improve-codebase-architecture
inputprocess_map
→define_nodes()
→define_edges(conditions)
→define_shared_state()
→define_guardrails()
outputgraph_spec · model_task_matrix
[phase] build_agent (×n)
agent(s)backend-api
skill(s)grill-me
inputnode_spec
→prompt_engineering()
→unit_test(input → expected_output)
→integrate_in_graph()
↺if test.fail(): fix_prompt(); retry()
outputnode_validated · coverage_ok
[phase] resilience_test
agent(s)security-observability
inputcomplete_graph
→simulate_timeout_llm()
→simulate_api_down()
→inject_malformed_output()
→test_guardrails()
↺if graph.fail(case): patch_node(); retest()
outputresilient_graph · test_report
[phase] integration
agent(s)backend-api
inputclient_apis[] · credentials
→build_adapters()
→test_interface_contracts()
→validate_irreversible_guardrails()
outputstable_adapters · connected_pipeline
[phase] deploy
agent(s)devops-marketing
inputresilient_graph · connected_pipeline
→docker_build()
→scaleway_push()
→langfuse_activate()
→configure_alerts()
outputproduction_pipeline · dashboards · runbook
[phase] continuous_ticketing
agent(s)backend-api·ui-dashboard·devops-marketing
skill(s)grill-me
inputfeature_requests[] · bugs[] · optimizations[]
→create_ticket(request)
→agent_prioritize(tickets[]) → ordered_backlog
→agent_spawn(ticket) → agent.resolve(ticket)
↺grill-me(ticket) → human_review() → if approved: merge_and_deploy()
outputcontinuously_improved_pipeline · zero_regression
Typical stack

LangGraph

Stateful orchestration: cycles, conditional branches, native checkpointing

Claude API (Sonnet / Haiku)

Task-based routing: Haiku for fast triage, Sonnet for complex decisions

Python or TypeScript

Depending on existing context: LangGraph supports both natively

Langfuse

LLM tracing, cost per node, detection of the most frequently failing nodes

Redis

Cross-session state persistence, task queues, cache on expensive LLM outputs

Docker + Scaleway

Reproducible deployment, fr-par cloud, GDPR-native, per-second billing

Client inputs
  • Description of the process to automate (steps, decisions, exceptions)
  • Concrete examples of expected inputs and outputs
  • Access to existing APIs and systems (credentials, documentation)
  • Risk tolerance: which actions can be automated without human validation
Orchestration

Conditional LangGraph graph: each node returns a typed state, the orchestrator routes to the next node based on conditions. Correction loops on critical nodes: if the validator rejects the output, the extractor restarts. Systematic guardrail before any external action: email, ERP write, payment. Native checkpointing: if the process crashes, it resumes from the last stable node.

Expected outputs
  • Operational agent graph in production
  • Test suite covering nominal and degraded cases
  • Graph documentation (nodes, transitions, state, conditions)
  • Configured Langfuse dashboards (traces, costs, latencies)
  • Incident runbook (outages, rollback, escalation to human)
ROI measurement
Operator time80-95% reduction on the automated process
Input errors0 manual input errors on nominal cases
Availability24/7 without human intervention on covered cases
Marginal costDecreasing: continuous prompt optimisation via Langfuse
Self-learning loops

Langfuse traces → fragile node detection. We identify nodes with the highest error rate or latency. Each cycle produces an improved prompt version.

Continuous cost/latency optimisation. Nodes with stable outputs migrate to Haiku. Those requiring reasoning stay on Sonnet. The cost per run decreases each sprint.

Final objective

A pipeline that runs without human intervention on nominal cases, alerts on edge cases, and costs less and less as prompts are optimised. The human stays in the loop for high-stakes decisions: not for repetitive tasks.

Related resources

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