Services / AI Engineering
We build coordinated AI agents with distinct roles, shared context and controlled handoffs. For work that genuinely needs more than one agent, not more agents for their own sake.
Clear responsibilities. Bounded execution. End-to-end accountability.
When coordination earns its place
The objective is a better outcome. Separating responsibilities can help when a task spans different tools, evidence sources or checks. It also introduces routing errors, state conflicts and operating cost. We assess both sides before choosing the architecture.
A single agent with a few tools may be enough. Known sequences and exact rules often belong in ordinary software. Adding a second agent to restate the first agent's answer does not establish independent verification.
We compare the proposed system with a simpler baseline before expanding it.
Coordination patterns
Not every handoff needs a model to decide what happens next. We combine explicit workflow logic with agent decisions only where the task requires it.
A research agent gathers evidence, a drafting agent prepares an output and a reviewer checks it against defined criteria. Each stage receives a typed result rather than an unbounded chat history.
Useful for: Document review, proposal preparation and repeatable workflows with distinct stages.
Tradeoff: One weak stage can affect everything downstream. Validate the handoff and stop when required evidence is absent.
Business applications
These illustrative workflows show where coordination may help. They are not customer results or prebuilt products; feasibility depends on your systems, data and operating requirements.
Separate source discovery, evidence extraction and synthesis. A review step checks citations and missing information before a recommendation reaches your team.
People resolve material conflicts and approve consequential recommendations.
Coordinate account research, qualification and proposal preparation around shared CRM records. Keep facts, assumptions and suggested terms distinct.
Pricing, external messages and contractual commitments require the agreed approvals.
Specialists extract fields, match supporting records and check exceptions. An orchestrator routes discrepancies and prepares a review package.
Payments and financial record changes remain behind enforceable controls.
Use focused agents to retrieve permitted account context, investigate a request and draft a response, while one workflow tracks the case through handoffs.
Sensitive cases and actions beyond the playbook go to an accountable person.
Separate issue analysis, change preparation and test review in an isolated development environment, with evidence attached to a proposed change.
Independent review and CI checks remain required; an agent does not approve its own release.
Connect bounded procurement, operations and finance steps using clear owners and shared workflow state rather than disconnected assistant conversations.
Use deterministic automation for known rules and retain people at policy exceptions.
A worked example
An illustrative procurement workflow. The system prepares a decision with supporting evidence; it does not grant agents authority to choose suppliers or spend money.
Checks required inputs, creates a run record and delegates bounded tasks. Missing information returns to the requester.
Retrieves permitted quotes, supplier records and purchasing history. Returns source references and freshness metadata.
Checks the proposed request against supplied purchasing rules and budget records. Deterministic code applies exact thresholds.
Assembles a comparison and flags conflicting or incomplete evidence without inventing a resolution.
Approves, edits or rejects the proposal with its evidence. Changed terms trigger renewed checks where required.
Rechecks authorisation and current records, then creates the approved request through a scoped integration. Records the outcome or an exception.
Engineering foundations
Reliable coordination depends on explicit boundaries and external state. We design the handoffs and recovery paths alongside the individual agents, rather than treating them as something to add after the demo.
Give every agent a task definition, permitted tools and data scope. Separate read, propose, approve and execute capabilities. Permissions are enforced by the application and connected systems, not by agent instructions alone.
Specify required fields, evidence references, status and error types. Validate outputs before another agent consumes them. Incomplete or contradictory results become explicit exceptions, not instructions to improvise.
Keep business records and workflow state outside the conversation. Define who can update each field, version writes and detect stale inputs. Long-term memory needs access controls, retention and deletion rules.
Use task identifiers, ownership leases and checkpoints where appropriate. Bound retries and delegation depth. Plan for partial completion, unavailable tools and approvals that arrive after a workflow has changed.
Cap model calls, tool usage, elapsed time and repeated handoffs. Stop or escalate when evidence is insufficient or progress stalls. A longer discussion between agents is not proof of a better result.
Revalidate permissions and preconditions before consequential writes. Use duplicate protection where supported. Link each task, tool call, approval and system change to a run trace with redaction and retention controls.
Security & human oversight
Every additional agent creates another boundary to protect. One specialist's access should not become every specialist's access, and an orchestrator should not bypass controls to get the task finished.
Apply least privilege at retrieval and tool execution. Credentials stay out of prompts, and every consequential action is checked against the caller and workflow.
Documents, tool responses and agent outputs may carry hostile or mistaken instructions. Treat them as data, validate the schema and test cross-agent prompt injection.
Separate preparation from authorisation. Reviewers see the proposed change and evidence; no agent approves its own material action.
Minimise shared context, redact sensitive traces and define retention. Review provider terms and deployment constraints with your security team.
Pause a run, revoke tools and isolate failed tasks. Plan recovery for actions already completed; not every external change can be rolled back.
Name the workflow owner and escalation path. Technical controls support your obligations but do not confer compliance or certification.
Evaluation & observability
Good individual outputs do not guarantee a good final outcome. We evaluate the complete workflow against a simpler baseline, then inspect where coordination improves or degrades quality.
Correct delegation, complete outputs, source fidelity and appropriate escalation when a role lacks evidence or authority.
Task completion, rework, human review effort and business cycle time. Include cases that require refusal or handover.
Simulate unavailable specialists, stale state, partial writes, duplicate messages and hostile content. Check whether the system stops safely.
Track model calls, repeated handoffs, latency, tool failures and cost per completed task. Parallelism may reduce wall-clock time while increasing spend.
Use a shared run identifier across parent tasks, child tasks, tools and approvals. Record structured decision summaries and evidence, not a promise to expose private model reasoning. Calibrate automated scoring with human-reviewed cases.
Technology stack
Relevant options, not a mandatory bundle. Framework selection follows the required state model, integration boundaries, evaluation results and your team's operating capability.
Evaluate graph-based state, explicit handoffs, typed agent interfaces or role-oriented coordination. Select one primary approach based on control requirements, maintainability and tested behaviour.
Persist long-running work, checkpoints and approvals. Use transactional state and queues where needed; durable execution does not guarantee correct model output.
Choose models per task using evaluations, data terms and operating cost. Scope retrieval to the agent and user; avoid giving every specialist the entire organisation's knowledge.
Typed tool contracts, authorised integrations and reviewer interfaces. MCP can standardise tool access but does not provide your permission policy or make tools inherently safe.
Trace parent and child tasks, evaluate each role and the complete workflow, and test handoff failures. Calibrate automated scoring with human-reviewed examples.
Fit deployment to your existing platform, network boundaries and team. Assess private inference when required; orchestration alone does not keep data inside your environment.
We review framework maturity, licensing and provider terms during design. Protocol compatibility is not proof of interoperability, and changing models or orchestration frameworks requires regression evaluation.
Delivery approach
We work alongside your process and technical owners. Each phase makes the design more testable and gives you a clear decision about whether the additional coordination is worth keeping.
Map the task and compare a rules-based workflow, one agent and a multi-agent design. Identify responsibilities that genuinely benefit from separation.
Output: A recommended architecture, baseline and scoped success criteria.
Agree roles, permissions, handoff schemas, state ownership, approval points and stopping rules. Check integration and data readiness.
Output: A system map, tool contracts and an evaluation plan.
Implement the coordinator and essential specialists with traceable state and a reviewer interface. Exercise real exceptions, not only the happy path.
Output: A bounded working pilot and role-level tests.
Evaluate against the simpler baseline. Test failed workers, stale records, hostile inputs and budget overruns. Start read-only or in shadow mode where appropriate.
Output: A go/no-go report, deployment plan and outstanding risks.
Roll out with a named owner, monitoring and rollback procedures. Change roles or add specialists only when the evidence supports it.
Output: Runbooks, handover and an agreed support arrangement.
Handover & ongoing operation
Deliverables and responsibilities are agreed to scope. We plan for the people who will investigate failed runs, change a policy or update a model after launch.
Source code, role definitions, tool interfaces, state schemas and deployment configuration, with ownership and third-party licence terms set out in the agreement.
Versioned evaluation cases, trace configuration, acceptance results and known limitations. Documentation of what was tested and which risks remain.
Reviewer guidance, alert ownership, recovery procedures and training. Agree handover or ongoing support, including responsibility for infrastructure and changes.
Related services & guides
Explore focused agent development and the design practices behind controlled execution.
Before you commit
No. More agents introduce more calls, handoffs and failure modes. We prefer a single agent or deterministic workflow when it meets the requirement. Coordination needs to improve a measured outcome enough to justify its cost and complexity.
They can handle bounded tasks under defined policies, but business accountability stays with people. Autonomy varies by role and action. Consequential decisions, exceptions and access changes require the controls agreed for your workflow.
Through scoped retrieval, structured messages and external workflow state. Each role gets only the data it needs. We validate handoffs and define state ownership rather than copying every agent's conversation into a shared prompt.
No. A reviewer can miss errors, particularly when it uses the same model and evidence as the producer. We combine task-specific validation, conventional tests and human review where risk requires it. Agent consensus is not a substitute for evidence.
Yes. We can inspect routing, tool access, state, evaluations and failure handling. Sometimes the most effective improvement is fewer roles, tighter contracts or moving a step back to deterministic code.
We can start with a paid two-week sprint with agreed deliverables to test the architecture and a bounded path. Production scope, price and timing depend on integrations, data readiness and control requirements; the sprint is not a guarantee of a complete production system.
Code ownership, third-party licences, repository access and support responsibilities are specified in the engagement agreement. Handover includes the operating documentation and evaluation coverage agreed to scope, with named owners for incidents and changes.
Choose a sensible starting point
Bring a process, an existing prototype or a coordination problem. We'll assess the architecture, define boundaries and agree what a paid two-week sprint should prove.