AI Agents

Platform · Layer 03

Multi-model runtime, human in the loop.

Bedrock AgentCore and LangGraph run the agents, the routing picks the model per task, and a person stays on every action you flag.

07 components in this layer

  • In production× 6
  • Reference architecture× 1
01

Amazon Bedrock AgentCore

Managed runtime, identity and memory in your AWS account.

02

LangGraph orchestration

Every step, retry and hand-off is a node you can test.

03

Multi-model routing

Claude, GPT, Gemini, Nova, Llama and open-weights, by eval and cost.

04

Multi-agent orchestration

A supervisor delegates with typed hand-offs and one trail.

05

Human in the loop

Approval gates on anything you mark. Full context, one button.

06

Own voice stack

Parakeet, Qwen3 32B and Kokoro, with no per-minute vendor.

07Reference architecture

On-prem voice

The same stack on your GPUs, for calls that cannot leave.

In production: A real client, or DaCodes itself labeled as internal use, with a KPI measured in the Proof Ledger.

Runtime

What happens between a message and an action.

Six steps, whatever the channel. Each one is a node in the graph.

01

Receive

The channel adapter turns any channel into one event.

02

Look up

Tools over MCP read your systems with scoped credentials.

03

Reason

The router picks the model, the graph runs it with your rules.

04

Check

Guardrails, PII masking and thresholds, before any write.

05

Act or ask

It writes through scoped tools, or opens an approval with the evidence.

06

Log

Every step lands in the audit trail and the eval set.

Inbound · lead 7731
Demo data
  1. 00:00

    Form submitted from /agents/collections-renewals

  2. 00:04

    Enriched: 1,200 employees · insurance · México

  3. 00:09

    Classified: qualified, 91% confidence

  4. 00:31

    WhatsApp sent · reply received 02:10

  5. 03:48

    Meeting booked with the account owner

Under five minutes

Time to first contact 0:31 · meeting in 3:48 · routed round robin

Same runtime, every channelVoiceWhatsAppEmailWeb chatInternal apps

Own voice stack

Voice at a fraction of the market price.

Most voice agents die on the cost per minute. Ours runs on open models inside your cloud.

Check call · Unit 42
Demo data
Live call · 00:41
  • Good morning, this is the dispatch assistant for Route 18. Are you on schedule for the 14:00 delivery?

  • Running late, about twenty five minutes. Traffic on the highway.

  • Understood. I am moving the delivery window to 14:25 and notifying the customer. Anything else to report?

  • No, all good.

ETA updated in TMS

14:00 → 14:25 · customer notified on WhatsApp · dispatcher alerted

Own voice stack · about $0.035 per 15-min call, internal measurement, Aug 2026

TMS
What is inside
  • Parakeetspeech to text
  • Qwen3 32Breasoning
  • Kokorotext to speech

Per 15-minute call

ElevenLabs
$2.56 to $5.17
Own stack
$0.035

Per year, same volume, at group scale

Per-minute vendor
~$5M
Own stack
~$60K
Range at list prices

Source: DaCodes internal measurement, Aug 2026. Comparison at list prices; volumes modeled for a 130,000-employee group in México.

Questions

Architects ask.

The one that wins the eval for that task. A typical agent uses two or three: a strong model for reasoning and edge cases, a fast one for classification and extraction, and an open-weights model where cost dominates. You can see and change the routing in the repo.

You mark the actions that need a person. When the agent reaches one, it prepares the action with the evidence, opens an approval in the Console or in Slack or Teams, and waits. The person approves, edits or rejects; all three are logged.

No. Vapi, Retell and ElevenLabs plug into the same runtime. We recommend our stack when volume makes the per-minute price the problem, and we show you the measurement before you decide.

Talk to an architect

45 minutes with the people who run the platform. No slides.

We reply in under one business day.

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