Across recent industry data, 78% of enterprises are running AI agent pilots and only 14% get one to production. The demo works. The agent breaks when it hits real data, real APIs and users who don't follow the happy path. We build AI agents that connect to your actual systems, handle the edge cases your prototype ignored, and run without constant re-prompting.
We're a London-based AI agent development company working with CTOs, Heads of Product and VPs of Engineering at companies that have already proven the idea and now need the production system. AgentWise, our own AI agent, is the proof: a real agent in live production with scoped tool use, guardrails and observability built in from day one.
Agents, not API wrappersProduction-first architectureNamed technical lead, no handoffGuardrails and monitoring from day one
There is a visible epidemic of three OpenAI calls wrapped in marketing being sold as agents. We draw an honest line: an agent has tool use, state, guardrails and a reason to exist beyond the demo. If the workflow is better served by a chatbot or deterministic automation, we'll say so and point you to the cheaper tool.
Your prototype worked on clean test data. In production the customer ghosts you, the card declines, the data format changed last week and the upstream OCR is down. We design for the unhappy path first: retries with backoff, validated output schemas, graceful failure, structured escalation.
Start with a single well-defined agent. Split into multi-agent only when the context window saturates or roles genuinely diverge. 12 parallel agents with no validation is three senior engineers untangling a mess by hour four. We make the architecture call with you in writing, with the trade-off explained.
n8n, LangChain and vendor platforms are good at what they're good at. If your workflow is straight-through, well-documented and low-stakes, we'll tell you to use one. You hire us when the agent has to make judgement calls, read and write to your systems under scoped permissions and be defensible when it breaks.
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We map the workflow the agent will own: inputs, outputs, decisions, the systems it must talk to and every point where a human currently has to think. Discovery includes spikes on your real APIs to surface integration friction before you commit budget.
Output: agent specification, integration plan, guardrail model, success metrics and a fixed-scope build quote.
Where integration risk is high we run a time-boxed PoC on your data, your APIs, your IAM, not a sandboxed demo. The PoC is designed to fail fast on the things that usually kill agents: messy legacy data, rate limits, silent upstream failures, retrieval quality at scale.
We will tell you to stop if the economics don't hold. We have done this on our own engagements.
Build runs as a small senior team led by Michal Vavra, with AI and integration engineers embedded with your stakeholders. Each release ships behind feature flags with tool-call tracing, output schema validation, regression tests on a fixed eval set, guardrails and rollback in place from week one.
We deploy to production early, gated on guardrails rather than on demo quality.
At launch we wire in drift detection, anomaly alerts, cost telemetry, audit logs and on-call ownership. You receive runbooks, architecture documentation and a handover session with your engineering team.
Monthly retainer for monitoring and tuning, not a lock-in. Take the agent in-house whenever you're ready.
Frequently asked <strong>questions</strong> about AI agent development, integration, guardrails and cost.
A chatbot answers questions. A Zapier or n8n flow moves data along a fixed path. An agent chooses which tool to call, manages state across steps, handles ambiguity and knows when to escalate. If your workflow fits a flowchart and the data is clean, use an automation tool. If it needs judgement, that's agent work.
Discovery is fixed-fee from £5K. Production agent builds typically start at £30K and run six to ten weeks, though agents with many integrations and strict governance land higher. The driver is integration complexity, not model choice. We quote fixed-scope at the end of discovery.
Discovery is one to two weeks. A PoC on your real stack adds two to three weeks if the integration risk warrants it. Build is typically six to ten weeks to a live system behind feature flags. Agents with many integrations or high regulatory exposure take longer, and we'll say so in writing.
If your workflow is straight-through and your team has the bandwidth, use the platform. We're the right call when the agent has to integrate deeply with your systems, pass compliance review, survive edge cases and be operable at 2 AM. We'll tell you honestly if a platform is enough for what you're trying to do.
Guardrails by action class. Read is loose. Write is scoped and logged. Deploy and any irreversible action goes through human approval or an explicit policy check, with instant rollback on anomaly detection. Every action is logged to an audit trail. There is a kill switch.
We design for production from the start. The PoC runs on your data, your APIs, your IAM, not a sandboxed demo. We treat edge-case handling, output validation, monitoring and escalation as part of the build, not a Phase 2 nice-to-have. Across recent industry data, roughly 86% of agent pilots never make it. We've built the playbook on the other side of that.
Yes. Standard contract is full IP ownership for the client: source code, infrastructure-as-code, prompts, fine-tuning recipes where applicable. We document the system so it can be handed to your team or another vendor at any point. The only reason you stay is because the work is good.
