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Agency · OpenAI Agent BuilderFree audit

OPENAI AGENT BUILDER AGENCY FOR NO-CODE AI AGENTS THAT WORK

Hack'celeration is an OpenAI Agent Builder agency that ships GPT-5 agents without writing a backend. The team designs agent flows, tool catalogs, hand-off logic, evals and CRM integrations on OpenAI's new no-code agent platform. Average time from idea to production agent: 2 to 3 weeks. Replace what used to be a 3-month LangChain build.

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OpenAI Agent Builder Agency — workflow & automation.
Hack'celeration Agency

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Our agency · why us

Why pick an Agent Builder agency that ships fast

OpenAI Agent Builder is OpenAI's no-code platform for building agents on top of GPT-5, Tool use and the Assistants API. It launched in 2025 as a higher-level alternative to writing LangChain code. Visual flow editor, tool catalog, evals, multi-agent hand-off, deployment in a few clicks. For 70% of agent use cases, it ships faster than a custom build. The remaining 30% still need code, and the team knows where each boundary sits.

Hack'celeration has built 20+ agents on OpenAI Agent Builder since its launch, for SaaS, ecom, consulting and B2B services. The team owns the full chain: agent design, tool catalog, prompt engineering, CRM integrations, evals, governance, deployment. A field note: 6 out of 10 teams that try Agent Builder solo end up with agents that pass the demo and fail in prod. The miss is almost always the same: no eval suite, no fallback for off-script queries, no observability. The team adds those guardrails on day one. Crosslinks: OpenAI agency, AI agent agency, n8n agency, LangChain agency.

Agent Builder · agency services

What the team delivers on OpenAI Agent Builder

Agent design. The team translates a business use case (e.g. 'sales rep prep assistant') into an agent spec: goals, tools needed, hand-off triggers, success criteria, eval set. Without that spec, agents drift. The team writes it in a 1-pager before opening Agent Builder. Quick win: define the 5 questions the agent must answer correctly before writing a single prompt. Saves 2 to 3 days of trial-and-error.

Tool catalog and integrations. Agent Builder lets you plug in tools via OpenAPI specs or built-in connectors. The team builds tool catalogs that hit your CRM (HubSpot, Salesforce, Pipedrive), ticketing (Zendesk, Intercom), data warehouses (Snowflake, BigQuery), and internal APIs. Tool design matters more than people think: clear names, typed parameters, structured outputs, no leaky abstractions.

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Multi-agent hand-off. Agent Builder supports multi-agent flows where a triage agent routes to specialist agents (sales, support, billing). The team designs hand-off logic, shared context, and termination conditions so agents don't loop. Crosslink: AI agent agency.

Evals and governance. Every agent the team ships comes with a 50 to 200 example eval set, a regression suite that runs on every prompt change, audit logs of every conversation, and a kill-switch your ops team controls. No 'we deployed the agent and now we're scared to touch it'.

2-3 WEEKS
2-3 WEEKS
from spec to production agent on most use cases
-75%
BUILD TIME
vs equivalent custom LangChain backend
90%+ ACCURACY
90%+ ACCURACY
after eval-driven iteration on top 3 intents
Agent Builder · playbook

How the team ships an Agent Builder agent in 3 weeks

Week 1: use-case spec, success criteria, eval set draft (30 to 50 example interactions), tool catalog scope. Week 2: agent build in Agent Builder, tool integrations (OpenAPI specs, OAuth flows, error handling), prompt engineering, multi-agent hand-off if needed. Week 3: eval run, regression fixes, observability setup, deployment to staging then prod, runbook for your team. Quick win: pilot the agent in a sandbox with internal users for 1 week before going customer-facing. You catch the weird edge cases that no eval suite predicts.

Agent Builder · multi-team

Agent Builder across every business team

Sales. Account research agents, deal health scoring, follow-up drafters, pipeline cleanup. Sales reps walk into calls with a 200-word briefing pulled from CRM, news and LinkedIn signals.

Customer support. Tier-1 support agents handling 50 to 80% of common questions, escalating clean tickets to humans. The team has shipped support agents reaching 92% first-reply accuracy on internal benchmarks, integrated with Zendesk, Intercom and Front.

Ops and internal tools. HR onboarding bots, IT helpdesk agents, finance reporting assistants, procurement Q&A. Agents replace the 'random Slack thread asking the same question' pattern. Crosslink: automation agency, workflow creation agency.

92%
FIRST-REPLY
accuracy on support agents after eval-driven tuning
-60%
ESCALATION
rate on tier-1 support with proper agent design
50-80% AUTO
50-80% AUTO
handled tier-1 volume on common-question agents
Our agency · innovations

An Agent Builder agency that knows when to use code instead

Agent Builder is excellent for 70% of agent use cases. For the remaining 30% (very high volume, complex state machines, cost-sensitive workloads, EU sovereignty), the team falls back to LangChain with LangGraph, custom Python, or sovereign models on Mistral and Llama. The team gives you a straight answer at audit time, not a sales pitch for the latest OpenAI feature.

The team also pairs Agent Builder with workflow orchestration tools like n8n and Make for downstream actions: enrich the contact, sync to CRM, slack the rep, schedule a task. Agent Builder handles the conversation; n8n handles the plumbing. Cleaner separation, faster iteration, easier handoff to your in-house team. Crosslink: AI agent agency.

Frequently asked questions

01How does OpenAI Agent Builder compare to LangGraph or n8n?+
Agent Builder is no-code, fast to ship, tightly integrated with GPT-5 and OpenAI tooling. LangGraph is code-first, more flexible, better for complex state machines and multi-provider routing. n8n is workflow-first, excellent for triggers and integrations but lighter on conversational AI. The team picks based on complexity, control needs, and existing stack. Most projects use Agent Builder for the agent core and n8n for downstream automation.
02What does Agent Builder cost beyond the OpenAI API?+
Agent Builder usage is billed on top of GPT-5 token consumption. Per-agent monthly fees depend on volume and OpenAI's pricing tiers. For a typical mid-size B2B use case at 1k to 10k conversations/month, total cost lands at a few hundred to a few thousand EUR/month. The team optimizes via prompt caching, smaller models on hot paths, and Batch API for offline workloads. Total spend usually beats equivalent custom builds when factoring in dev cost.
03Can Agent Builder agents handle complex business logic?+
Yes, with discipline. The trick is to push complex logic into tools (TypeScript or Python functions hosted on your own infrastructure) and keep the agent layer focused on conversation, routing and orchestration. The team designs tools with clear contracts, typed I/O and structured errors. Agent Builder handles the chat; your tools handle the math. Cleaner, more testable, easier to evolve.
04Is Agent Builder GDPR-compliant for European companies?+
It runs on OpenAI's infrastructure. OpenAI offers EU data residency and signs DPAs. For maximum sovereignty, the team routes sensitive workloads to alternative agent stacks on Mistral or Llama self-hosted. Most B2B use cases run fine on Agent Builder with EU residency. The team flags edge cases (PII, regulated data, public sector) at audit time.
05Can I migrate an existing LangChain agent to Agent Builder?+
Yes, common ask in 2025. The team audits the existing agent, identifies what fits Agent Builder cleanly (conversation, tool use, hand-off) and what doesn't (custom state machines, multi-provider routing). Typical migration: 3 to 5 weeks for a mid-size agent. The team keeps the migration backward-compatible with feature flags and A/B testing before cutover.
06How long to ship a production agent?+
2 to 3 weeks for a single-agent use case with 3 to 5 tools, evals and CRM integration. 4 to 6 weeks for multi-agent flows with hand-off, governance and high-volume readiness. The team works in 2-week sprints with a demo each. Faster is possible if you accept smaller scope or use an existing tool catalog.
07Do agents replace SDRs, support reps or knowledge workers?+
Replace, no. Augment, yes. Agents handle the repetitive 50 to 80% of tier-1 work, route complex cases to humans with full context, and free knowledge workers for the parts that need judgment. The team designs hand-off triggers so customers and reps both know when to escalate. The economics: agents shift the cost curve, they do not remove the human layer.
08What does the first 60min audit cover?+
Review of your 2 to 3 best agent use cases, current CRM and tooling, success criteria, governance constraints. Live mock-up of one agent in Agent Builder during the call. You leave with 4 to 6 concrete recommendations and a rough scoping. No upsell, no slide deck. Book a slot and bring your ops or product lead.
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