// SERVICE · CUSTOM INTERNAL AGENTS

Multi-stepagentsthatfinishthejob.

WHO THIS IS FOR

For teams that need agents to do real, multi-step work (research, support, ops) and want them production-grade, not autocomplete-with-extra-steps.

Production agents with the eval, observability, and safety scaffolding to actually trust them in front of customers. Tool-calling, memory, recovery from failure, and a human-in-the-loop path for everything that matters.

Book a scoping call →See how we work

// USE CASES

What we actually build.

PROBLEM

Research agent

Strategy team spends 4 hours researching every prospect. Output is inconsistent.

SOLUTION

Agent with web search, internal CRM access, and a structured brief template. Reviewed before delivery.

STACK

ClaudeBrave SearchPython

TIMELINE

4–5 weeks

PROBLEM

Support agent (front-line)

L1 support handles 60% repetitive questions. Tier-2 backlog growing.

SOLUTION

Agent with knowledge-base RAG + ticket-tool calls + clean escalation path. Always cites sources.

STACK

ClaudeZendeskpgvector

TIMELINE

5–7 weeks

PROBLEM

Voice agent

Inbound calls for appointment booking, after-hours triage, or status updates flood your team.

SOLUTION

Voice agent with tool calls, transfer-to-human, transcript logging, and refusal on out-of-scope asks.

STACK

VapiOpenAI RealtimeTwilio

TIMELINE

4–6 weeks

PROBLEM

Knowledge-base agent

8 years of Notion + Slack + Drive content. Nobody can find anything.

SOLUTION

Agent with permissioned retrieval across sources, citations, and an evals harness on real questions.

STACK

ClaudepgvectorSlack

TIMELINE

5–6 weeks

PROBLEM

Sales / ops copilot

Reps need an internal 'ask anything' copilot: pipeline status, account history, next steps.

SOLUTION

Agent with read-only access to Salesforce + Gong + emails + full audit log. Read-only by design.

STACK

OpenAISalesforceGong

TIMELINE

6–8 weeks

// SAMPLE DELIVERABLE

What you walk away with.

agents-handoff/ — 14 files · 2.4 MBmain

📁 agents-handoff/

📄 README.md

📄 architecture.md

📁 evals/

groundtruth.jsonl

dashboard.html

regression.yml

📁 src/

agent.py

tools.py

prompts.yaml

📁 deploy/

terraform/

monitoring.json

📄 runbook.md

🎬 walkthrough.loom

# agents-handoff

## Stack: Claude · OpenAI · pgvector · Python

## What's in here

Complete handoff for the custom agent build. Includes tool

allowlists, audit log schema, and kill-switch config.

## Agents included

- research-agent — web + CRM brief generation

- support-agent — RAG + escalation + citations

- kb-agent — permissioned cross-source retrieval

## Running evals

$ python evals/regression.py --suite groundtruth.jsonl

## Safety defaults

Read-only by default. Write actions require explicit confirmation.

PII filtering active. Rate limits set in monitoring.json.

## Loom walkthrough

24-minute recorded handoff at walkthrough.loom

// FAQ

Common questions for this kind of work.

What guardrails do you put on agents?

Read-only by default. Tool allowlists, structured output validation, output filtering for PII, hard rate limits, full audit log, and a kill switch. Write actions always require explicit user confirmation unless you opt out.

How do you measure if an agent is 'working'?

Three layers: (1) per-step evals (does the tool call have the right args?), (2) end-to-end task evals (did it finish the goal?), (3) production traces with sampled human review. We ship the dashboard.

Multi-agent or single-agent?

Default single-agent with tools. Multi-agent only when there's a real boundary in capability or context window. 'Agent of agents' is usually a red flag. Extra coordination cost without quality gain.