Book discovery call
Agentic FDE pod

Engineers inside your team who take AI agents
from pilot to production

A small forward deployed pod works in your repos, your data, and your approval chains. Every agent ships with evaluations, guardrails, and a handover plan your team can run.

Book discovery call
Related clients

Why do so many AI agent pilots
never reach production?

The demo works, the workflow does not

Pilots run on clean sample data. Real records carry exceptions, missing fields, and approvals the prototype never saw.

Nobody can say how good the agent is

Without evaluation sets, every prompt change is a guess. Teams stall because they cannot prove the agent is safe to widen.

Integration is the hard 80%

Agents need scoped access to ERP, CRM, and document stores. Vendor tools stop at the API boundary your systems actually sit behind.

The partner leaves, the knowledge leaves

Consulting teams hand over slides. Your engineers inherit code they did not write and cannot safely change.

What an agentic FDE pod
builds with you

Agentic FDE pod

Use-case scoping and autonomy design

We map the workflow step by step and decide where the agent acts alone, where it drafts for review, and where a human must approve. The output is an autonomy matrix your risk team can sign.

Agentic FDE pod

Multi-agent system engineering

Planner, worker, and reviewer agents built on your chosen model and framework, with tool calls, retries, and state handled explicitly. Code lives in your repositories from the first commit.

Agentic FDE pod

Evaluation harness and quality gates

Golden datasets drawn from real cases, automated scoring, and regression checks in CI. Every prompt or model change runs against the same bar before it reaches users.

Agentic FDE pod

Enterprise system connectors

Scoped, audited connections to ERP, CRM, ticketing, and document stores, including MCP servers where they fit. Agents get least-privilege access that security can review line by line.

Agentic FDE pod

Guardrails and human-in-the-loop review

Input and output validation, policy checks, and review queues that route low-confidence actions to people. Reviewers see the agent’s reasoning and sources before approving.

Agentic FDE pod

Observability and cost controls

Tracing for every agent run, dashboards for accuracy and latency, and spend alerts per workflow. You see what each agent did, what it cost, and where it failed.

Agentic FDE pod

Handover and team enablement

Pairing with your engineers throughout, runbooks for every agent, and a staged transfer of ownership. The pod exits when your team can extend the system without us.

Have a pilot that stalled before production?

Bring it to a discovery call and we will tell you what it needs to ship.

Review my AI pilot

How an agentic FDE pod
moves from workflow to production

  1. We sit with the people who run the workflow today, collect real cases, and agree on success metrics. We also confirm data access and approval rules before any code is written.

    Deliverables: Workflow map, autonomy matrix, success metrics, evaluation dataset v1

  2. The pod builds one agent end to end inside your environment, wired to real systems and scored against the evaluation set. Your engineers pair on the work from day one.

    Deliverables: Working agent in staging, evaluation report, architecture decision record

  3. We add guardrails, review queues, tracing, and cost alerts, then release to a limited user group. Autonomy widens only when evaluation scores and reviewer feedback support it.

    Deliverables: Production release, monitoring dashboards, incident runbook

  4. Additional agents reuse the same connectors, evaluation harness, and controls. Ownership moves to your team in stages until the pod is no longer needed.

    Deliverables: Agent backlog, ownership transfer plan, team enablement sessions

What an embedded agentic pod
helps you achieve

Ship agents, not prototypes

Work happens in your production environment from the start, so there is no rebuild between pilot and release.

Prove quality before you widen access

Evaluation sets and regression checks give risk and business owners evidence instead of anecdotes.

Keep humans where judgment matters

Review queues route uncertain or high-impact actions to people, so automation grows without losing control.

Know what every agent costs

Per-workflow tracing and spend alerts show cost and accuracy side by side, so you scale what pays back.

Own the system after we leave

Your engineers pair throughout and receive runbooks, so the capability stays when the pod exits.

Ready to see this in your own workflows?

Book discovery call

Agent platform, in-house, or FDE pod:
the honest comparison

Each path works for some teams. The right one depends on how specific your workflows are and how much engineering capacity you have today.

Criterion Off-the-shelf agent platform In-house team tkxel agentic FDE pod
Time to first production agent Fast for standard use cases Slow while hiring and learning Weeks, scoped per workflow
Fit to your workflows Limited to vendor templates Full Full, built in your stack
Evaluation and guardrails Vendor-defined Built from scratch Included from week one
Who owns the code Vendor You You, from the first commit
Ongoing cost Per-seat or per-run licence Full team salaries Time-boxed pod, then your team
Fails when Workflows leave the template AI talent is hard to hire Nobody internal is ready to take ownership

Success stories in spotlight

IndustryAgentic FDE pod

[Placeholder] Metric-led result for an agentic workflow, followed by the mechanism that produced it.

“Placeholder quote — replace with a real client quote.”
— Director of Operations
IndustryWorkflow agentification

[Placeholder] Second agent case study headline that leads with the metric.

“Placeholder quote — replace with a real client quote.”
— VP Engineering
IndustryEnterprise connectors

[Placeholder] Third case study headline that leads with the metric.

“Placeholder quote — replace with a real client quote.”
— Chief Technology Officer

Engineers who ship inside your systems,
not isolated AI demos

Talk to an FDE lead

Production from the first sprint

We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.

Senior pods, not rotating benches

Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.

Governance built in, not bolted on

Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.

Measured against your metrics

Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.

We've been recognized by the best, year after year

  • Inc. 5000
  • Top 15 Inspiring Workplaces
  • Titan Business Platinum (AI & Automation)
  • Financial Times
  • Mogul People Leader
  • Forbes Coaches Council
  • ISO 27001
  • ISO 20000
  • ISO 9001
  • CMMI DEV 3

Ready to put your first agent into production?

Clutch[Placeholder] Clutch badge and rating — add the official widget
“Placeholder testimonial — replace with the global set from tkxel.com.”
— Knowles
“Placeholder testimonial — replace with the global set from tkxel.com.”
— Sterne Kessler

By submitting, you agree to our Terms and Privacy policy.

Frequently asked questions

What exactly is a forward deployed engineer?

A forward deployed engineer works inside the client’s environment rather than from a separate delivery centre. They join your standups, commit to your repositories, and talk directly to the people who run the workflow. The model comes from product companies that embed engineers with customers to make complex software work in real conditions. At tkxel it means a small pod that builds, ships, and then hands over production systems.

How is an FDE pod different from staff augmentation?

Staff augmentation adds people to your backlog and you direct the work. An FDE pod owns an outcome, such as a specific agent reaching production, and brings its own delivery method, evaluation tooling, and architecture experience. The pod also plans its own exit, so the engagement ends with your team owning the system rather than depending on extra headcount.

Which models and frameworks do you use?

We work with the model and cloud your organisation has approved, including Claude, GPT, and Gemini models through their APIs or through Amazon Bedrock, Google Vertex AI, and Azure. Framework choice follows the workflow; we use established agent frameworks where they help and plain code where they add complexity. Everything is documented so your team can swap components later.

How large is a pod and how long does it stay?

A typical pod is three to five people: a lead engineer, one or two AI engineers, and a data or integration engineer, with a delivery lead shared across the engagement. Most first agents reach a limited production release within two to three months. Larger programmes keep the pod longer while ownership transfers in stages.

Can you guarantee the agent will be fully autonomous?

No, and we would be cautious of anyone who does. Many workflows include decisions that should stay with a person for regulatory, financial, or customer reasons. We design autonomy per step, release with human review where accuracy is not yet proven, and widen it only when evaluation results support the change. Some steps may stay human-approved permanently, and that is often the right outcome.

How do you handle our data and security requirements?

The pod works inside your environment under your access controls, so data does not leave systems you approve. Connectors use least-privilege, audited access, and we align with your security review process from the first week. tkxel holds ISO 27001 certification, and we can work within SOC 2, HIPAA, or industry-specific controls your organisation already runs.

What do we need to have in place before starting?

A named business owner for the workflow, access to real examples of the work, and at least one engineer who will eventually own the system. Clean data is helpful but not required; the discovery phase identifies data gaps and whether they block the first release or can be handled with review steps.