Building agentic AI platforms that survive production

An AI agent is a loop: a model reads context, decides on an action, calls a tool, observes the result and decides again. That loop is what makes agents useful for open-ended work. It is also what makes them hard to run in production, because every iteration is a chance to go off course.

Enterprises that succeed with agents stop treating each one as a project and start building a platform: shared capabilities every agent uses, so quality, safety and cost are managed once rather than reinvented per team.

Why agents fail after the demo

• Unbounded loops: an agent retries a failing tool call until it hits a token or cost limit.
• Context drift: long conversations or large retrievals push the instructions that matter out of the model’s attention.
• Over-permissioned tools: an agent that can read a database can often write to it, too.
• Silent quality regressions: a new model version or prompt tweak changes behaviour and nobody notices for weeks.
• No audit trail: when a customer or regulator asks why the agent did something, there is no record to answer with.

The five capabilities of an agent platform

Across the agentic systems we build, the same five capabilities decide whether an agent is still trusted six months after launch.

• Evaluation: a versioned test set for every agent, scored automatically on each change to prompts, tools or models, with release gates on the results.
• Guardrails: input and output checks for prompt injection, sensitive data and policy violations, applied before anything reaches a user or a system of record.
• Tool governance: a registry of tools with scoped credentials, least-privilege permissions and explicit approval steps for actions that change data or spend money.
• Observability: traces of every step — prompts, retrieved context, tool calls, latency, tokens and cost — linked to the business transaction they served.
• Orchestration: timeouts, retry budgets and human-in-the-loop steps managed by a workflow engine rather than hand-written loops.

Choose models per task, not per company

As an OpenAI and Anthropic partner, we are often asked which model to standardise on. Our answer is to standardise the platform and choose models per task. A routing layer lets a fast, inexpensive model handle classification and extraction while a frontier model handles complex reasoning — and lets you switch providers without rewriting agents.

Protocols such as the Model Context Protocol (MCP) help here: tools are exposed once, with consistent permissions, and any compliant model or agent framework can use them.

Design for humans in the loop

The goal is not full autonomy on day one. It is the right level of autonomy for each decision. We start agents in a recommend-only mode, measure how often reviewers accept their output, and widen autonomy step by step as evaluation data supports it. Approvals, overrides and escalations are captured as data, which in turn improves the evaluation set.

A practical starting point

• Pick one high-volume process with clear success criteria and a human already doing the work.
• Build the evaluation set from real historical cases before writing the first prompt.
• Put the agent behind the shared platform from the start — tracing, guardrails and tool registry included.
• Launch in recommend-only mode, then expand autonomy on evidence.

Agentic AI rewards organisations that invest in the platform early. The first agent costs a little more; every agent after it ships faster, safer and cheaper.