AI agents that actually do the work.
Autonomous agents for ops, support, sales, and engineering. Built with MCP, tool-use, and human-in-the-loop where it matters. We design for the boring 20% (auth, audit, rollback, observability) that makes the magic 80% safe to deploy.
AI agents take action on systems — they can read your calendar, file tickets, query databases, send Slack messages. The interesting part is the planning loop; the production part is the audit, rate-limiting, rollback, and approval flows that make this safe in front of real customers and real revenue.
- ·Agent architecture (planning loop, tool definitions, memory)
- ·MCP server integration for your internal systems
- ·Tool catalog with permission scoping per agent
- ·Human-in-the-loop approval flows for high-stakes actions
- ·Audit log of every action taken by every agent
- ·Rollback / undo paths for reversible actions
- ·Cost + latency observability per agent + per tool
- ·Eval suite + sandbox environment for safe iteration
- ◇Anthropic Claude with computer use / tool use
- ◇Model Context Protocol (MCP) servers
- ◇OpenAI Assistants / GPT function calling
- ◇LangGraph / Inngest (orchestration + durable execution)
- ◇Browser automation (Playwright) for web tasks
- ◇Custom MCP servers for your internal systems
Map agent capabilities, integrations needed, approval boundaries, cost + latency ceilings.
Define each tool with permissioning, idempotency, audit trail. Design rollback paths.
Planning loop, retrieval over your context, tool execution, evals.
Adversarial testing, prompt injection defense, rate limiting, escape hatches.
Phased rollout starting with internal users + low-stakes tasks. Expand as confidence grows.
- ◆Audit logging of agent decisions + actions
- ◆Permission scoping aligned to RBAC
- ◆Approval requirements for material actions
- ◆PII redaction in agent prompts
- ◆Vendor due diligence for model providers
Agent Tool Catalog — a documented inventory of every tool the agent can use, with: idempotency status, rollback strategy, audit log fields, permission scope, approval requirements, and rate limit. Plus a worked example trace of a typical agent run showing every tool call + decision + outcome.
What can agents actually do today?+
Real-world deployed examples: triage support tickets, draft sales follow-ups, reconcile invoices, monitor cloud costs, run security playbooks. Anything well-defined with clear success criteria + reversible actions.
What about prompt injection?+
Layered defenses: input sanitization, tool permission scoping, output validation, rate limiting, human approval for material actions. We assume injection will happen and contain blast radius.
Should an agent have my admin credentials?+
No. Agents should have purpose-scoped tokens with the minimum permissions to do the job. We design these scopes as part of the build.
How do you handle errors / mistakes?+
Every action that is reversible has a rollback path. Every action that is irreversible requires explicit human approval. The agent's job is to do the work; the human keeps the steering wheel.
Build with Claude vs GPT vs Llama?+
Claude leads on tool-use reasoning today. GPT-4o is competitive. For cheap/fast simple tasks, Llama-via-Groq works. We pick per-tool, not per-agent.