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AIEngineersLabs
Agentic AI

Long-running agents with durable state and human-in-the-loop.

Most agentic demos work for the demo and break in production — no checkpointing, no idempotency, no plan for the third tool call that returns a 500. We build agents that run for hours, recover from failures, and surface their reasoning so your operations team can intervene.

Agentic systems take a goal, decompose it, call tools, observe results, and loop. They’re useful for workflows where rules-based automation breaks: claims triage, research assistants, document orchestration, customer ops. The production constraints are different from RAG — long-running execution, durable state, idempotency, replay, observability of the agent’s plan.

What you get

Agents that ship past the demo.

  • An agentic system in production

    Orchestrator, tool layer, memory store, HITL approval gates, retry/replay logic — running in your environment under your audit framework.

  • Trace observability

    Every agent run is replayable. Operations teams can debug a bad outcome by walking the plan, the tool calls, and the observations.

  • Failure-mode coverage

    Non-deterministic retries, tool timeouts, circular plans, malformed outputs — handled, instrumented, and bounded by configurable budgets.

  • Eval gates in CI

    Pre-deploy harness that runs the agent against curated tasks and blocks regressions on plan quality, tool selection, and outcome accuracy.

  • A handover

    Your team owns the prompt library, the tool catalog, the trace pipeline, and the runbook. We don't keep the keys.

What we ship

Engineering, not stagecraft.

Orchestration
Anthropic tool use, OpenAI Assistants, LangGraph, or custom — picked per use case based on durability and HITL requirements.
State
Postgres for durable runs, Redis for short-term memory, S3-compatible for artefacts. State is replayable.
Tooling
Typed tool interfaces (JSON schema), per-tool timeouts, retry budgets, dead-letter queues. Tool catalog versioned in git.
Tracing
Langfuse or custom OTEL pipeline. Every trace includes plan, tool calls, observations, costs, and outcome.
HITL
Approval gates at high-impact actions — payouts, external messages, regulated decisions. Configurable per workflow.
Eval
Scenario-based agent evals + LLM-as-judge for plan quality. Block deploy on regression.
Engagement model

How it runs

Timeline12–20 weeks to production
Pod size1 architect · 2–3 engineers · 1 MLOps
DeliverablesAgent · tool catalog · trace pipeline · runbook
Pricing postureDiscovery → managed-pod build
Proof
A top-10 insurer's claims team handled simple claims in days and complex claims in weeks. We shipped an agentic triage system that classifies, requests missing documents, and routes — with HITL on every payout decision. Time-to-disposition for simple claims fell under one hour. SOC 2 review passed on first audit.

Top-10 insurer · Agentic claims triage · SOC 2 audited

Frequently asked

What buyers actually ask

Are agentic systems production-ready?
The frontier models are. The engineering around them is the work. We've shipped agents handling regulated workflows — the discipline is in failure-mode coverage, observability, and HITL design, not the model choice.
How do you handle non-determinism?
Idempotent tool calls, durable run state, replayable traces, and eval gates that catch regressions before deploy. Non-determinism is a property of the model — production design assumes it.
What's the cost of a failed run?
Bounded by your retry budget, your tool budget, and your HITL gates. Every cost-relevant decision is configurable. Most engagements ship with per-run cost ceilings and per-tenant rate limits.
Can the agent call our existing internal tools?
Yes — that's most of the work. We wrap your APIs with typed tool interfaces, add timeouts and retries, and document each tool for the agent's tool catalog.
Next step

Talk to an engineer, not a salesperson.

30 minutes. No slides. Bring an architecture, a stalled roadmap, or a vendor proposal you want a second opinion on. We'll tell you what we'd do.