Agents under your control that take action.
Autonomous and semi-autonomous AI agents that reason, plan, use your tools, and complete multi-step work — with oversight and guardrails built in.
Single-Agent Workflows
An agent that reasons over a goal, plans the steps, calls the right tools, and works through a multi-step task — with retries and stop conditions so it finishes or fails cleanly, instead of looping forever.
Multi-Agent Orchestration
Specialized agents — planner, researcher, executor, reviewer — coordinated through a controlled workflow, so complex work is split across roles with explicit handoffs instead of one overloaded prompt doing everything.
Tool & Function Calling
Agents act through typed tools — functions, API calls, database queries, searches — with validated inputs and outputs, so the agent works through interfaces you define rather than improvising on a live system.
MCP & System Connectivity
We connect agents to your systems using the Model Context Protocol (MCP), the open standard for exposing tools to agents — built on the APIs your systems already provide, never brittle screen scraping.
Human-in-the-Loop Controls
Approval gates, confidence thresholds, and risk-tagged actions, so consequential or irreversible steps pause for a person. This is the line between a useful agent and a liability.
Guardrails & Observability
A policy envelope — allowed tools, spend limits, scopes, loop limits — plus a kill switch and full action logging, so every step the agent took is bounded and traceable after the fact.
The Autonomy Spectrum
Autonomy is a dial. You set it.
An agent that acts is only safe if you decide how far it can go. We build across four levels of autonomy — each with the human oversight and guardrails that level warrants. You choose the level per task, and you can always pull it back.
More human control
More agent autonomy
Click any level to see what we implement to keep it safe.
Across the Production Gap
Agent systems built to run on real tasks — with retries, timeouts, stop conditions, and error handling — not a flashy demo that falls apart on the fifth step or the first edge case.
You Control the Autonomy
Every agent ships at the autonomy level you choose, from suggest-only to bounded-autonomous. You can dial it down per task, and the agent never exceeds the authority you have granted it.
No Runaway Agents
Hard policy envelopes, risk-gated actions, and a kill switch mean an agent acts inside the limits you set and escalates anything outside them — instead of improvising on a live system.
Every Action Is Traceable
A complete action log records what the agent did, with what inputs, and who approved it — so you can review, debug, and explain any decision after the fact, not guess at what happened.
You Own the System
Built on open agent frameworks with a documented architecture and a multi-model design — so you are not locked to a single model vendor and not dependent on us to keep the agent running.
Key Capabilities
- Agent design: goal framing, planning, and tool selection
- Single-agent task automation with retries and stop conditions
- Multi-agent orchestration (planner, executor, reviewer roles)
- Tool and function calling with typed, validated interfaces
- Model Context Protocol (MCP) tool and system connectivity
- Retrieval grounding so agents act on facts, not guesses
- Human-in-the-loop approval gates and confidence-based routing
- Policy envelopes: allowed tools, spend limits, scopes, loop limits
- Agent observability: run tracing, action logs, and a kill switch
- Multi-model agent design (Claude, GPT, open-source) — no lock-in
Technologies
Engagement Models
Agent Feasibility Sprint
- Use case and task decomposition for one workflow
- Tool and system-surface assessment (what the agent can act on)
- Autonomy-level and guardrail design
- Working prototype of the agent on your workflow
- Go / no-go recommendation with production estimate
Production Agent Build
- Single or multi-agent system for a defined workflow
- Tool / function calling and MCP connectivity
- Human-in-the-loop approval gates and confidence routing
- Guardrails: policy envelope, loop limits, and kill switch
- Observability, run tracing, and action logging
- Handover documentation and runbook
Agent Build + Managed Operation
- Everything in Production Agent Build
- Ongoing monitoring and incident response
- Guardrail and prompt tuning as tasks evolve
- Model evaluation and upgrades as new models ship
- Direct Slack/Teams channel with the agent team
Frequently Asked Questions
How is agentic AI different from generative AI or a chatbot?
A generative AI system or chatbot produces an output — an answer, a summary, a draft. An agent takes actions: it reasons over a goal, plans the steps, calls tools, and works through a multi-step task, observing each result before deciding the next move. The distinction matters for risk. A wrong answer from a chatbot is embarrassing; a wrong action from an agent — sending the wrong message, changing the wrong record — can be costly or irreversible. That is exactly why we build the autonomy levels and guardrails on this page: an agent that acts is only useful if it acts under control.
What stops the agent from doing something it should not?
Several layers, and you choose how strict they are. Each tool the agent can use is defined and risk-tagged, so consequential or irreversible actions route to a human for approval rather than running automatically. The agent operates inside a policy envelope — an allow-list of tools, spend limits, scopes, and loop and step limits — and an operator kill switch can stop it at any moment. Anything outside the envelope causes the agent to stop and escalate instead of improvising. Every action is logged with its inputs and approver. The autonomy spectrum on this page shows the four levels of control you can dial between.
Will the agent actually work in production, or just in a demo?
Production reliability is the design target, not an afterthought. The most common failure modes for agents — looping forever, hallucinating a tool call, or failing silently mid-task — are handled with explicit stop conditions, typed and validated tool interfaces, retries with backoff, and timeouts. We start with a feasibility sprint that runs the agent on your real workflow to find where it struggles before a full build, so you get an honest verdict on reliability rather than a polished demo that breaks on the fifth step.
How does the agent connect to our tools and systems?
Through interfaces you control. We build agent connectivity using the Model Context Protocol (MCP) — the open standard for exposing tools and systems to agents — together with typed function and API calls, on top of the APIs your systems already provide. We do not screen-scrape, and we do not claim pre-built connectors to specific third-party products: each tool is defined, validated, and risk-tagged for your environment. That keeps the agent working through stable interfaces and keeps the connections something you own and can audit.
Do we own the agent, or are we locked into you or one model vendor?
You own it. Agents are built on open frameworks (such as LangGraph) with a documented architecture, defined tools, and a multi-model design so you can route between Claude, GPT, or open-source models rather than depending on a single provider. Handover includes a runbook and knowledge transfer so your engineers can operate, extend, and adjust the agent without us. If you choose the managed option, that is a convenience, not a dependency — you can take operations in-house at any point.
We are not sure agents are the right fit for our problem — where do we start?
Start with the Agent Feasibility Sprint. In two weeks we decompose your target workflow, map the tools and systems an agent would need to act on, design the autonomy level and guardrails that fit, and build a working prototype on your real workflow. You get an honest go / no-go verdict with a production estimate. Sometimes the right answer is a simpler automation or a generative-AI assistant rather than a full agent — and saying so is a legitimate outcome. You leave with a clear decision and the evidence behind it.
Ready to put an agent to work — safely?
Book a 30-minute call. We will discuss your workflow, which tasks an agent could take on, and the autonomy level and guardrails that fit your risk tolerance.