Agentic AI Systems
AI that takes action, not just AI that responds.
Real production agents with proper governance, error handling, and human-in-the-loop design.
In plain English.
We build AI agents that do work (research, draft, route, update records, trigger workflows) inside your real systems, with the controls that make autonomy safe. An agent that can act is only valuable if you can trust what it does, so governance and human-in-the-loop checkpoints are part of the design, not an afterthought.
Our agents run on Claude and OpenAI models, orchestrated with proper tool use, retries, and guardrails, and connected to the tools where work happens: Salesforce, Slack, Linear, and your database. Every action is logged and reversible.
We design for the failure modes that sink most agent projects: hallucinated actions, runaway loops, and silent errors. You get an agent that's measurably more reliable than a script and genuinely accountable for what it touches.
When you need this
- A repetitive, multi-step workflow is eating your team's time.
- A chatbot isn't enough, and you need AI that actually completes tasks.
- You want automation that reasons across tools, not brittle if-then rules.
- You tried agents before and they were unreliable or unaccountable.
The deliverables, plainly stated.
- Production AI agents built on Claude and OpenAI with tool use
- Integration with Salesforce, Slack, Linear, and your database
- Human-in-the-loop checkpoints for high-stakes actions
- Guardrails against hallucinated actions and runaway loops
- Full action logging, audit trail, and reversibility
- Evaluation harness to measure agent reliability over time
Typical duration
30-day cycles (1 to 3 cycles typical)
Investment band
$$$Significant investment
We scope in bands, not fixed numbers. Final pricing follows a quick scoping call.
A process built for this service, not a generic playbook.
- 01
Map the workflow
We break the target workflow into discrete steps, define which can be autonomous, and decide where a human must approve.
- 02
Build the agent loop
We implement the agent on Claude or OpenAI with robust tool use, retries, and guardrails against loops and bad actions.
- 03
Wire in the tools
We connect the agent to systems like Salesforce, Slack, and Linear, logging every action so it's auditable and reversible.
- 04
Evaluate and tune
We run an evaluation harness against real cases, measure reliability, and tighten guardrails before widening autonomy.
Team composition
A lead AI engineer specializing in agent design, a full-stack engineer for integrations, and a solutions architect on governance and guardrails.
Tools & frameworks
- Claude and OpenAI for agent reasoning and tool use
- Salesforce, Slack, and Linear integrations
- Native Bridge agent evaluation harness
- Datadog for action monitoring and alerting
What we tie this engagement to.
Every engagement carries a revenue-tied KPI. These are the outcomes this service typically anchors on.
A multi-step workflow reliably handled by an accountable agent
Measurable hours reclaimed from manual, repetitive work
A safe, logged, reversible automation your team trusts
Works with your stack
We deliver Agentic AI Systems inside the tools you already run.
Agentic AI Systems: common questions
What is an agentic AI system?
It's an AI system that takes actions (researching, drafting, updating records, triggering workflows) across your real tools, rather than just answering questions. Native Bridge builds these on Claude and OpenAI with tool use, guardrails, and human-in-the-loop checkpoints.
How do you keep agents from doing the wrong thing?
We design for the common failure modes up front: guardrails against hallucinated actions and runaway loops, human-in-the-loop approval for high-stakes steps, full action logging via Datadog, and reversibility so any action can be undone.
What's the difference between an agent and a chatbot?
A chatbot responds with text. An agent completes tasks: it reasons across tools like Salesforce, Slack, and Linear, takes actions, and is held accountable for them through logging and evaluation.
How do you measure whether an agent is reliable?
We build an evaluation harness that runs the agent against real cases and tracks success rate, error types, and intervention frequency, so reliability is measured continuously rather than assumed.
How long does it take to build a production agent?
We work in 30-day cycles; a focused agent for a single workflow typically reaches production in one to two cycles, with additional cycles to widen autonomy as reliability data accumulates.
Often paired with this.
Ready to put Agentic AI Systems to work?
Tell us where you are and we'll tell you what's blocking revenue.