Services/Service 01

Production-grade agents for the work that actually matters.

From research copilots to autonomous operations crews, we engineer agentic systems with the guardrails, observability, and reliability your business demands.

Agentic AI Development · reference architecture
live · 110ms p95
INGRESSREASONINGACTIONAPI GatewayREST · gRPC · WebSocketIdentity / RBACOIDC · SCIM · Audit logData LakeS3 · Snowflake · KafkaVector Indexpgvector · PineconePlannerSlow · DeliberateMemoryEpisodic · SemanticExecutorFast · DeterministicTool Registry14 typed toolsGuardrailsPolicy · ApprovalsExternal APIsCRM · ERP · StripeObservabilityTraces · Replay · Evals
Latency
110ms
Throughput
42k req/min
Eval score
0.94
Cost / 1k
$0.12
The problem

Most "AI agents" never make it past the demo.

Prompt chains break in production. Tools hallucinate side effects. Hand-offs lose state. Teams ship pilots, not platforms.

Brittle prompt pipelines with no recovery paths
No observability — silent failures in production
Unsafe tool use against real customer data
No clean separation between policy, planning, and execution
Our approach

A reference architecture for agents that ship.

We bring a battle-tested architecture: planner-executor split, typed tools, replayable runs, eval harnesses, and human-in-the-loop checkpoints by default.
Planner / Executor split

Slow, deliberate planning models drive fast, deterministic executors. Reliable and auditable.

Typed tools + guardrails

Every tool is schema-validated. Side-effectful actions sit behind policy gates and approvals.

Eval harness

Golden traces, regression sets, and CI for agent behavior — like tests, but for reasoning.

Observability

Run-level tracing, cost attribution, and failure replay tooling baked in from day one.

Process

From kickoff to production,
in the open.

  1. 01

    Workflow discovery

    Identify the highest-value workflows where autonomy compounds.

  2. 02

    Tooling & policy design

    Define the action surface, permissioning, and human checkpoints.

  3. 03

    Agent assembly

    Build the planner-executor split, memory, and routing.

  4. 04

    Eval & red-team

    Run adversarial evals and behavioral regression suites.

  5. 05

    Phased rollout

    Ship to shadow mode, then assisted, then autonomous.

  6. 06

    Operate & improve

    Continuous evaluation and tool expansion.

Tech stack

The tools we reach
for first.

Models
  • Claude
  • GPT-5
  • Gemini
  • Open-source LLMs
Orchestration
  • LangGraph
  • CrewAI
  • Custom DSLs
Infra
  • AWS Bedrock
  • GCP Vertex
  • Kubernetes
  • Temporal
Observability
  • LangSmith
  • OpenTelemetry
  • Datadog
  • Custom replay
Benefits

What you get when this lands.

Compounding ROI

Each tool you add to the action surface multiplies what every agent can do.

Audit-ready

Every decision is traceable, replayable, and attributable.

Safe by construction

Policy gates, dry-run modes, and human approvals where stakes are high.

Operator confidence

Dashboards that show what agents are doing — without reading raw logs.

Recent work

A real engagement
in this shape.

Nuvo Payments · Fintech

A new fraud-detection platform, deployed in 14 weeks.

We rebuilt Nuvo’s fraud platform around a real-time agentic decision engine — cutting false positives by 64% while accelerating settlement.

−64%
False positives
110ms
Decision latency
$8.4M
Annual savings
Read the case study
Nuvo Payments · agent
v2026.5
Run trace · #ag_28f1
running
plan.invoke
34ms
tool.search_kb
212ms
tool.fetch_user
88ms
guardrail.policy
12ms
tool.send_email
Eval score
0.94
Cost / run
$0.012
Approvals
3 / 3
Model
claude-sonnet-4-6
Questions

Things people
usually ask.

A focused workflow ships in 6–10 weeks. We start with shadow mode and graduate to autonomy as evals stabilize.

Related

Services that
often pair with this.

Let’s scope what this looks like for you.

A 30-minute technical conversation. No slides, no salespeople.