AI agent development,
secure from day zero.
Function Zero is an AI agent development company. We design and build custom AI agents and multi-agent systems for real workflows, with the security work most teams add later (threat modeling, guardrails, and red-teaming) built in from the first sprint.
Agents that do real work.
Inside real systems.
From one agent that owns a single job to coordinated teams of specialist agents, every build is designed around the systems it touches and what could go wrong if it misbehaved.
Single-task agents
Agents that own one job end to end, such as triaging tickets, qualifying leads, reconciling records, or drafting reports. Narrow scope, measurable results.
Multi-agent systems
Orchestrated teams of specialist agents (planners, researchers, builders, and reviewers) that split up complex work and check each other's output.
Tool use and integrations
Agents that act in your stack through well-scoped tools: CRMs, databases, ticketing, internal APIs, and dev tools, each with least-privilege access.
Retrieval (RAG) and memory
Answers grounded in your documents and data, with retrieval that respects permissions and memory that is scoped, inspectable, and deletable.
Workflow automation
Self-healing pipelines with retry logic, circuit breakers, and fallback chains, so one failed step degrades gracefully instead of stopping the line.
Human-in-the-loop controls
Approval steps for high-impact actions, clear escalation paths, and an audit trail of what each agent did and why.
Hardened before
it ships.
An agent that can read your data and call your tools is a new attack surface. We treat it like one from the first design review.
- Threat modeling during architecture: what each agent can read, call, and change, and what happens if an attacker steers it.
- Least-privilege tools and credentials for every agent, so one compromised agent can't reach everything.
- Guardrails on inputs and outputs: prompt injection defenses, output validation, and checks that stop sensitive data leaving.
- Red-teamed before production. Every agent faces real attack vectors, including prompt injection, data leakage, and tool misuse, before launch. See how we test.
- Monitored after launch, with tracing, quality and cost metrics, alerts, and a plan for incidents.
From blueprint
to production.
Discover
We map the workflow, the systems involved, and where an agent adds the most value, with a first read on the security risks.
Architect
Agent topology, tools, data access, and defense layers, written up as a blueprint with scope, timeline, and cost before any build starts.
Build & red team
Iterative development with security testing in every cycle, against the real systems the agent will use.
Deploy & monitor
Production rollout with monitoring, incident response, and continuous hardening as your data, tools, and models change.
What you get.
No black boxes.
We build with OpenAI, Anthropic, open-source models, and custom fine-tunes, choosing per task on quality, latency, cost, and where your data is allowed to go. You own the result.
- A written blueprint covering architecture, tools, data access, and the threat model
- Production agents running in your infrastructure, with source code and prompts you own
- Guardrails, permissions, and monitoring, configured and documented
- Red-team findings from before launch, with each fix verified
- Runbooks for operating, updating, and rolling back your agents
Questions,
answered.
What does an AI agent development company do?
It designs and builds AI agents: software that uses large language models to plan work and take actions through tools, such as searching records, updating a CRM, or filing a ticket. At Function Zero that also means securing them, with threat modeling, guardrails, red-teaming, and monitoring as part of the build.
What is the difference between an AI agent and a chatbot?
A chatbot answers questions. An agent acts: it can call tools, change data, and hand work to other agents. That makes agents more useful, and it is why their permissions and guardrails need as much design as their prompts.
How long does it take to build an AI agent?
It depends on how many systems the agent touches and how much risk its actions carry. Discovery ends with a written blueprint that sets out scope, timeline, and cost before the build starts.
Which AI models do you use?
Whichever fits the job. We work with OpenAI, Anthropic, open-source models, and custom fine-tunes, and keep the model behind a clean interface so it can be swapped as better options appear.
Can your agents work with our existing systems?
Yes. Agents connect through APIs and scoped tool integrations to the systems you already use, such as CRMs, databases, ticketing, and internal tools, with only the access each task needs.
Who owns the code and prompts?
You do. You get the architecture, code, and prompts, along with documentation of the security decisions behind them.
Build it. Break it.
Govern it.
Ready to deploy AI
you can actually trust?
Tell us about your workflow. We'll show you what's possible — and exactly what it takes to keep it secure.