What is a good first agent to build in an enterprise?

Pick a workflow with clear inputs and outputs that is currently done manually — triage, routing, customer-query classification. Avoid creative or open-ended workflows for the first ship; those need more eval infrastructure.

How long does the first enterprise agent take to ship?

4–8 weeks for the first production agent, including discovery, design, build, observability, and handoff. The Day-1-to-Day-30 stability work doubles the timeline if it is skipped on the way in.

Should we train our team or hire a consultant?

Both, in order. Consulting to design and build the first system end-to-end; training so your team can ship the next five without external help. Most clients run them in parallel.

Topic Pillar

Enterprise AI Automation.Agents that replace operational work, not just write emails.

Enterprise automation is where agentic AI pays back fastest: operational workflows, support and service pipelines, ERP integrations, content-at-scale. The wins are concrete — hours saved per ticket, headcount that scales sub-linearly, decisions logged for audit. The patterns matter more than the model.

● 13 cluster pages· 6 posts· 1 notes· 6 updates

Where automation pays back fastest

Operational triage (routing, processing, escalation), customer support agents that hit ticket systems directly, and content-plus-SEO pipelines (research → outline → write → review). These are workflows with clear inputs, measurable outputs, and decisions that can be logged.

Why IT services teams ship faster than expected

IT services teams have the right combination: existing ops workflows to automate, domain context to write good tool descriptions, and developer headcount to maintain the systems. The blocker is rarely capability — it is usually scoping the first agent narrowly enough to actually ship.

What we deliver on consulting engagements

Architecture design, production-grade implementation using Claude API and MCP, full observability (Langfuse), structured outputs (Pydantic), retry semantics, and a real handoff so your team can maintain and extend it. Working agents, not slides about agents.

6 blog posts

Deep dives on Enterprise AI Automation

Production

The cheapest LLM call is the one you do not make — GitHub's 19-62% token cut, decoded

GitHub published an instrumented analysis of their agentic CI workflows and reported 19-62% token-cost reductions. The savings are the headline. The technique — pre-agentic data fetching and tool-registry hygiene — is the story most teams will miss.

May 11, 20265 min

Read the post Multi-Agent

Why I am replacing supervisor patterns with handoffs

Supervisors looked clean on paper and shipped slow in production. Handoffs read messier in the code but recover better when an agent loses the plot. Two real systems and where supervisors still earn their keep.

Apr 26, 20266 min

Read the post Production

Prompt caching is not optional anymore — measuring a 47% cost drop

A walkthrough from a client engagement: identifying stable prefixes, restructuring the system prompt for cacheability, and the telemetry that proved caching was actually working.

Apr 19, 20264 min

Read the post Production

The agent observability stack we ship to every client

Traces, spans, evals, cost-per-completed-task, and the one dashboard panel that catches 80% of regressions. Vendor-agnostic — covers Langfuse, Honeycomb, and rolling your own.

Mar 28, 20267 min

Read the post Multi-Agent

Haiku 4.5 made our router 5x cheaper. The trade-off matters

Replacing Sonnet with Haiku in the dispatcher role cut our orchestration cost dramatically. It also cost us in two specific places I did not predict.

Feb 22, 20265 min

Read the post Production

Eval datasets: stop testing your agents on the happy path

If your eval set is the demos you showed the client, you are testing the wrong thing. How we build evals from production failures and the minimum viable suite to ship.

Jan 19, 20266 min

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Visual breakdowns on Enterprise AI Automation

Tooling

You merged it. Then it failed.

Modern pipelines review code before merge — automated review, security scan, and test generation on every PR.

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6 ship-news updates

Latest in Enterprise AI Automation

Architecture

GitHub cuts agentic CI workflow costs 19-62% by pruning tools and moving data-fetch outside the LLM loop

May 11, 2026 · via GitHub Engineering Blog

MCP

MCP 1.0 ratified — official SDKs in Python, TypeScript, Go, Rust, Java, .NET

May 2, 2026 · via modelcontextprotocol.io

Claude

Sonnet 4.6 update: cheaper tokens, sharper tool calls, fewer retry loops

Apr 24, 2026 · via Anthropic

Tools

Cursor 1.0 stabilises background agents and ships a review-and-merge workflow

Apr 18, 2026 · via Cursor

Research

Anthropic research: when to use supervisor vs. swarm patterns in multi-agent systems

Apr 15, 2026 · via Anthropic Research

OpenAI

OpenAI Agent Builder GA — pricing finally competitive for enterprise tool use

Apr 12, 2026 · via OpenAI

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Enterprise solutions

How Enterprise AI Automation ships in our engagements

The pages below are the buyer-focused, conversion-grade versions of this topic — deliverables, methodology, ROI, security considerations, and CTAs to scope a real engagement.

Solution