Our Methodology

12-factor agents for production

We design agents like real systems: prompts you can version, context windows you can inspect, tools you can audit, and state you can replay.

No magic, just engineering discipline for LLM-native workflows.

Philosophy

Workflows for machines, not improv

We don't assign job titles to agents or make them click around pretend dashboards. We map intent to functions, remove glue steps, and enforce contracts in code.

Treat the model as a callable function, not a teammate

Flatten steps; fewer hops mean fewer failure points

Prefer JSON contracts over UI clicks

Expose tools directly—no proxy dashboards

Define signatures and error paths for every tool

Let humans speak freely, but keep the system typed

Drop the office metaphor; route inputs to execution

Before → After

Collapse the workflow

Example: summarising highlights from a source and saving them to a knowledge base.

Human-mimic (6+ steps)

1Export highlights
2Paste into chat
3Ask for summary
4Copy response
5Open notes app
6Paste and tag

Function-mapped (2 steps)

fetch_highlights(source)

write_summary(destination, tags)

Contracts and error handling live in code, not in a prompt. The model speaks JSON, not small talk.

Engagement

Function Mapping Sprint

Collapse one workflow in 10 days.

You bring one broken agent workflow; we dismantle the roleplay and replace it with callable functions, JSON contracts, and live telemetry that proves the collapse.

−78%

Steps Removed

−9m

Latency Drop

Book a Sprint

Days 0-2

Field Signal

Mine transcripts, tickets, and runbooks. Annotate every step that exists only for human cognition.

Days 3-7

Function Index

Rewrite the flow as schemas + execution contracts. Route language inputs to deterministic functions.

Days 8-10

Collapse Proof

Ship a runnable demo + dashboard showing steps removed, latency dropped, and failure points eliminated.

Architecture

Four pillars of agent design

The same patterns powering our voice stack and API routes show up in every engagement. Each pillar is an artifact you can review, test, and evolve.

Prompts

System prompts are versioned, named, and documented. A/B test agent behaviour, roll back changes, and tie outcomes back to prompt versions.

system-prompts.ts

Context

We own the context window: which RAG documents, which history, which query. Observability into token budgets, trimming, and what the model actually saw.

context-manager.ts

Tools

Tools are first-class, typed definitions with explicit parameters and user-facing messages. Every call is auditable.

tool-definitions.ts

State

Conversation state is managed via a reducer, so you can replay sessions, debug decisions, and plug in persistence.

conversation-state.ts

In Practice

How this shows up in client work

Whether we're building a realtime voice agent, a back-office copilot, or a mesh of task agents, we follow the same pattern: define prompts, curate context, expose tools, wire state, and plug into your observability stack.

You gain confidence that agents will behave consistently across channels, and your teams gain a shared vocabulary to reason about failures, regressions, and improvements.

Architecture diagrams and design docs for every agent surface

Evaluation harnesses that mirror real workflows, not toy prompts

Integration with existing observability tools (logs, traces, metrics)

Clear hand-offs between human operators and agents

See It Live

Explore the 12-factor voice agent

The same principles power our Athena voice stack. In demos, we can walk through how prompts, context, tools, and state come together in realtime.

Talk to Athena Book a Demo