Applied AI
AI Agents & Agentic Workflows
Agents that plan, call your tools, and finish real work, with approvals, retries, and a full audit trail.
A chatbot answers. An agent acts. We build agents that break a goal into steps, call your APIs and databases along the way, ask a human when the stakes are high, and recover when a step fails. The interesting engineering isn't the prompt. It's the orchestration around it: tool schemas, state, retries, idempotency, cost ceilings, and a trace of every decision so you can prove what happened and why.
What's included
- Tool-calling & orchestration
- Human-in-the-loop approvals
- MCP integrations
- Durable, resumable runs
What you get out of it
- Multi-step processes that used to need a person now run unattended, with a human only on exceptions
- Every run is traced end to end: inputs, tool calls, costs, and the final decision
- Clear cost and latency ceilings per run, so spend never surprises you
Agents that survive contact with production
Demos break on the second edge case. We design agents around failure: bounded plans, typed tool schemas, idempotent side effects, retries with backoff, and a checkpointed state machine so a run can pause for approval and resume hours later. Every step is traced, evaluated against a regression suite, and gated by guardrails before it touches a customer or a system of record.
run: start (goal=reconcile invoices, budget=$0.40)plan: 4 steps · tools[erp.search, ledger.match, slack.notify]step 1/4 erp.search(period=2026-07) -> 812 rowsstep 2/4 ledger.match() -> 806 matched · 6 exceptionsgate: human approval requested (6 exceptions)step 4/4 slack.notify(#finance) -> deliveredtrace: 11 calls · 41s · $0.18 · guardrails PASS
What we build
Multi-step orchestration
Planner/executor architectures with typed tools, explicit state, and checkpointing, so a long-running job can pause, wait for a human, and pick up exactly where it left off.
MCP servers & tool integrations
We expose your internal systems to agents through the Model Context Protocol and typed tool schemas, so one integration works across every model and client you use instead of being rewritten per vendor.
Multi-agent systems
Specialist agents with narrow, well-defined jobs coordinated by a supervisor, used where a single do-everything prompt becomes unreliable and impossible to evaluate.
Approvals & guardrails
Risk-scored actions, human-in-the-loop gates on the ones that matter, allow-lists on destructive tools, and output checks that stop an agent before it does something expensive.
Evaluation & regression suites
A fixed set of real tasks the agent must keep passing. Every prompt, model, or tool change runs against it, so you upgrade with evidence instead of hope.
Technologies we work with
- Anthropic Claude
- OpenAI
- Google Gemini
- MCP
- Agno
- Langfuse
- Temporal
- Python
- TypeScript
- FastAPI
- Postgres
- Redis
Frequently asked questions
A chatbot produces text. An agent decides on a sequence of actions and executes them against your real systems: searching, writing records, sending messages. Then it reports what it did. That means it needs permissions, guardrails, and an audit trail, which is most of the work.
Three layers: tools are scoped and allow-listed so the agent simply cannot reach what it shouldn't; high-risk actions require a human approval before they run; and output guardrails check the result before anything is committed.
We pick per project rather than by fashion. Typically that means the model vendor's own SDK plus a thin orchestration layer we control, with MCP for tool integrations. We avoid frameworks that hide the control flow, because that is exactly the part you need to debug.
Per-run token and dollar budgets, smaller models for routing and classification, caching of stable context, and tracing that attributes spend to individual steps so the expensive ones are obvious and fixable.
Next step
Need ai agents & agentic workflows?
Tell us what you're trying to build. We'll give you an honest read on the approach, the effort, and what it costs to run.