AI Agent Development — Production-Ready Autonomous Agents
AI agent development is the process of building autonomous software agents that use large language models to reason, call tools and APIs, and complete multi-step tasks with minimal human input. Perimattic builds these as fixed-scope, production-deployed systems — not prototypes.
Overview
What is AgentForge?
An AI agent is an LLM-powered system that can plan a task, choose and call the right tools or APIs, act on the results, and hand off to a human when needed — rather than just answering a single prompt. Perimattic's AgentForge delivers these agents production-ready with orchestration, guardrails and monitoring built in.
Where a chatbot answers questions, an agent *does work* — it retrieves data, triggers actions in your systems, chains steps together, and knows when to escalate. That autonomy is powerful and risky, which is why a production agent needs far more than a clever prompt: it needs tool integration, orchestration logic, fallback handling, guardrails against unsafe actions, and monitoring you can trust. That engineering gap between a demo agent and a production agent is exactly what this service closes.
The AgentForge delivery kit codifies the parts most agent projects re-invent — a tested orchestration layer, a guardrail pattern, a monitoring dashboard, a human-handoff flow — so we spend the engagement on your specific workflow, not on scaffolding the plumbing every time. That is what makes 4–8 weeks a real number rather than a marketing wish.
What You Get
What you get
AgentForge is a fixed-scope engagement to design, build and deploy a working autonomous agent inside your environment. We map the workflow the agent will own, wire it to your tools and APIs, build the orchestration and guardrail layer, and deploy it with monitoring so you can see what it is doing and why. You get a running agent and the runbook to operate it — not a notebook that only works on our machine. Every action the agent takes is logged, every escalation is auditable, and every integration is documented so your team can extend or replace the agent later without our involvement.
Scope
Fixed scope — what is and is not included
| Not included | Included |
|---|---|
Building net-new backend systems the agent depends on | Agent workflow design and boundary definition |
Ongoing prompt tuning beyond the handover window (available as retainer) | Tool and API integrations (agreed set at scoping) |
Unlimited integrations beyond the agreed scope | Orchestration built on LangChain or LlamaIndex |
Data collection or labelling for downstream model training | Guardrails, fallback logic, and human-handoff flow |
Production deployment inside your environment | |
Monitoring dashboard with action-level audit trail | |
Operator runbook and handover training |
| Timeline | Best for | Tech stack |
|---|---|---|
| 4–8 weeks | Support automation, internal ops agents, research and analysis agents, sales development and lead-qualification agents | LangChainLlamaIndexFunction-calling LLMs (OpenAI, Anthropic, Azure)Vector store (Qdrant, Pinecone)Your existing APIs |
Process
How it works
Workflow mapping
We define exactly what the agent owns — inputs, actions, tools, escalation points. A one-page workflow specification becomes the contract for the build, so scope drift is impossible.
Integration build
We connect the agent to the tools and APIs it needs to act. Every integration is written to a shared pattern so failures are handled consistently and observability is uniform across tools.
Orchestration and guardrails
The reasoning logic, tool-selection policy, fallback paths, and safety limits on what the agent can do are built. Guardrails include rate limits per tool, blocked actions, PII redaction where relevant, and a mandatory human-handoff path for anything outside the agreed boundary.
Deployment and monitoring
We ship to production inside your environment with observability so every action is traceable. Alerts fire on unusual behaviour — spike in escalations, tool failure rates, or off-policy attempts.
Handover
A runbook, operator training, and a clean line to a retainer if you want ongoing tuning. Your team owns the running system from day one after go-live; we are on call for the first 30 days.
Use Cases
Use cases
Every one of these is a real, bounded workflow — which is what makes it buildable to fixed scope. Vague ambitions become unbuildable; specific processes become agents.
Customer support agents
Resolve tickets end-to-end — pull order status, refund history, subscription state; take the action; escalate to a human only when the situation is outside the agent boundary. Reduces mean time to resolution and shifts human agents to the harder cases.
Internal operations agents
Pull data from CRMs, ERPs, cloud tools, and finance systems; trigger actions across them; produce a daily or event-triggered summary. Common patterns include finance close automation, IT joiner-mover-leaver workflows, and sales-ops routing.
Research and analysis agents
Gather sources on a defined topic, synthesise them against a template, cite them, and hand off to a human for the final judgment call. Used by consulting firms, competitive intelligence teams, and R&D groups to compress the first draft cycle.
Sales development agents
Qualify inbound leads against your ICP, enrich the record, draft an initial outreach, and route to the right salesperson. Sits alongside your existing SDR team, not instead of them.
Results
Proven in production
AI agents deployed through AgentForge are running in production across support automation and internal operations workflows for our client base. Every agent ships with the same guardrails, monitoring, and handover discipline — which is what makes the outcomes repeatable rather than one-off wins.
Why Us
Why Perimattic for AI agent development
Agents are where most vendors ship a demo and call it done. We ship production: guardrails, monitoring, handoff, and a runbook — backed by the DevOps discipline Perimattic has built since 2018. Enterprise-grade security (PII handling, access control, audit logging) is default, not an upsell. And because the AgentForge delivery kit is refined across every engagement, the same team, the same architecture, and the same 4–8 week window applies to every build.
Frequently Asked Questions
Frequently asked questions
What is the difference between an AI agent and a chatbot?
A chatbot answers a single question at a time using a large language model. An AI agent plans a task, chooses which tools to use, calls them, acts on the results, and knows when to hand off to a human. In shorthand: a chatbot talks; an agent acts. Perimattic AgentForge builds agents — production-deployed systems that take actions inside your environment with guardrails, monitoring, and human-handoff paths.
How long does an AI agent take to build?
AgentForge is a fixed 4–8 week engagement. The exact window depends on the number of integrations, the complexity of the guardrails, and whether the agent has to be deployed inside your VPC or as a managed service. The window is committed at scoping — it does not slip in delivery.
What can an AI agent integrate with?
Anything with an API — CRMs (Salesforce, HubSpot, Zoho), ERPs (SAP, Oracle NetSuite, Microsoft Dynamics 365, Odoo), cloud tools (AWS, Azure, GCP services), support desks (Zendesk, Freshdesk, ServiceNow), internal databases, message queues, or your own custom services. Perimattic's DevOps and integration roots go back to 2018, so unusual or legacy targets are usually addressable.
Are AI agents safe to deploy?
They are safe when the guardrail layer is engineered properly, which is exactly what AgentForge focuses on. Every agent ships with tool-level rate limits, an explicit blocked-action list, PII redaction where relevant, off-policy attempt logging, and a mandatory human-handoff path for anything outside the agreed workflow boundary. The agent cannot take actions your team has not sanctioned.
Which frameworks do you use?
LangChain and LlamaIndex are our default orchestration frameworks — both are mature, well-supported, and well-understood in the AI engineering community. Vector stores are typically Qdrant. The LLM layer is model-agnostic — OpenAI, Anthropic, Azure-hosted models — chosen against your privacy, cost, and latency requirements. If you have a house standard, we work with it.
Can the agent work with our internal APIs?
Yes. Internal APIs are the most common integration target — agents that only touch third-party SaaS tools are rare in enterprise engagements. We add authentication, retry logic, and error handling to every integration and document the contract so your team can extend the agent without breaking it.
Do you offer ongoing support?
Every AgentForge build includes 30 days of post-go-live support at no extra cost. Beyond that we offer an optional retainer covering prompt tuning, integration updates as your source systems evolve, guardrail refinements based on production behaviour, and adding new tools to the agent's toolkit. Retainers are month-to-month with 30-day notice.
How much does an AI agent cost?
AgentForge is quoted as a fixed scope against the workflow specification produced at scoping — no hourly billing and no open-ended invoices. Contact us for a costed proposal for your specific workflow, integration count, guardrail complexity, and deployment target.
Related
Related in the Perimattic AI Suite
AgentForge pairs naturally with these sibling modules.
Enterprise RAG
Agents often sit on top of a RAG knowledge base for grounded reasoning.
ExploreAI Automation
AI automation is the retainer-friendly sibling — start with an agent, then wire more workflows through IntelliFlow.
ExploreAI Blueprint
If the workflow to automate is not yet defined, start here.
ExploreScope your AI agent build — fixed price, fixed timeline
One-page scope. One-page price. One clear delivery window.
Scope your AI agent buildOr email us at sales@perimattic.com
Perimattic AI Suite — All Services